The document discusses a proposed CDMA-based watermarking scheme that aims to improve robustness and message capacity. It begins with an overview of digital watermarking phases and concepts. It then discusses applying CDMA techniques to watermarking, modeling video as a bit plane stream, defining the watermark and spreading it using m-sequences. The watermark is inserted into video bit planes determined by a pseudorandom sequence. Experimental results showed the proposed scheme has higher robustness than conventional approaches under different attacks. Wavelet transforms and their use in watermark extraction are also briefly covered.
This document discusses different techniques for digital image watermarking, including in the spatial and frequency domains. It provides an overview of watermarking concepts and applications. It then describes two watermarking algorithms - one that embeds watermarks in the spatial domain by modifying pixel intensities in selected image blocks, and another that embeds watermarks in the wavelet domain by modifying selected wavelet coefficients. Both algorithms are described step-by-step and include watermark insertion and extraction procedures. Results are provided showing the performance of the algorithms under different attacks in terms of normalized cross-correlation between the original and extracted watermarks.
DWT-DCT-SVD Based Semi Blind Image Watermarking Using Middle Frequency BandIOSR Journals
This document summarizes a research paper that proposes a semi-blind image watermarking technique using discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD). The technique embeds a watermark in the middle frequency band of the DWT domain of a host image. It modifies the singular values of the DCT coefficients of the middle frequency band using singular values of the DCT transformed watermark. The watermark can then be extracted from the watermarked image using inverse processes. The technique was tested on various attacks and showed robustness, with correlation values between the extracted and original watermarks ranging from 0.5308 to 0.9665 and PSNR values indicating impercept
A DWT based Dual Image Watermarking Technique for Authenticity and Watermark ...sipij
In this paper we propose a DWT based dual watermarking technique wherein both blind and non-blind algorithms are used for the copyright protection of the cover/host image and the watermark respectively. We use the concept of embedding two watermarks into the cover image by actually embedding only one, to authenticate the source image and protect the watermark simultaneously. Here the DWT coefficients of the primary watermark (logo) are modified using another smaller secondary binary image (sign) and the midfrequency coefficients of the cover/host image. Since the watermark has some features of host image embedded in it, the security is increased two-fold and it also protects the watermark from any misuse or copy attack. For this purpose a new pseudorandom generator based on the mathematical constant π has been developed and used successfully in various stages of the algorithm. We have also proposed a new approach of applying pseudo-randomness in selecting the watermark pixel values for embedding in the cover image. In all the existing techniques the randomness is incorporated in selecting the location to embed the watermark. This makes the embedding process more unpredictable. The cover image which is watermarked with the signed-logo is subjected to various attacks like cropping, rotation, JPEG compression, scaling and noising. From the results it has been found that it is very robust and has good invisibility as well.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
it is used for security purpose using two level dct and wavelet packet denoising .based on digital image processing.the software based on matlab.it is used for high security purpose.
DWT Based Audio Watermarking Schemes : A Comparative Study ijcisjournal
The main problem encountered during multimedia transmission is its protection against illegal distribution
and copying. One of the possible solutions for this is digital watermarking. Digital audio watermarking is
the technique of embedding watermark content to the audio signal to protect the owner copyrights. In this
paper, we used three wavelet transforms i.e. Discrete Wavelet Transform (DWT), Double Density DWT
(DDDWT) and Dual Tree DWT (DTDWT) for audio watermarking and the performance analysis of each
transform is presented. The key idea of the basic algorithm is to segment the audio signal into two parts,
one is for synchronization code insertion and other one is for watermark embedding. Initially, binary
watermark image is scrambled using chaotic technique to provide secrecy. By using QuantizationIndex
Modulation (QIM), this method works as a blind technique. The comparative analysis of the three methods
is made by conducting robustness and imperceptibility tests are conducted on five benchmark audio
signals.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document discusses a digital video watermarking technique using discrete cosine transform (DCT) and perceptual analysis. It proposes embedding a binary watermark in the DCT domain of video frames. A mathematical model is developed to insert a visible watermark into video frames in the DCT domain while considering characteristics of the human visual system to minimize perceptual quality impact. Experimental results show a watermarked video frame with the watermark logo embedded at different positions. The technique aims to provide copyright protection for digital video applications.
This document summarizes a research paper that proposes a novel video watermarking scheme using discrete wavelet transform (DWT) and principal component analysis (PCA). The scheme embeds a binary logo watermark into video frames for copyright protection. PCA is applied to blocks of two bands (LL-HH) resulting from DWT of video frames. The watermark is embedded into the principal components of LL and HH blocks at different levels. Combining DWT and PCA improves the watermarking performance by distributing the watermark bits over sub-bands, increasing robustness to attacks. The scheme provides imperceptible watermarking that is robust against various attacks such as geometric transformations and brightness/contrast adjustments.
This document discusses different techniques for digital image watermarking, including in the spatial and frequency domains. It provides an overview of watermarking concepts and applications. It then describes two watermarking algorithms - one that embeds watermarks in the spatial domain by modifying pixel intensities in selected image blocks, and another that embeds watermarks in the wavelet domain by modifying selected wavelet coefficients. Both algorithms are described step-by-step and include watermark insertion and extraction procedures. Results are provided showing the performance of the algorithms under different attacks in terms of normalized cross-correlation between the original and extracted watermarks.
DWT-DCT-SVD Based Semi Blind Image Watermarking Using Middle Frequency BandIOSR Journals
This document summarizes a research paper that proposes a semi-blind image watermarking technique using discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD). The technique embeds a watermark in the middle frequency band of the DWT domain of a host image. It modifies the singular values of the DCT coefficients of the middle frequency band using singular values of the DCT transformed watermark. The watermark can then be extracted from the watermarked image using inverse processes. The technique was tested on various attacks and showed robustness, with correlation values between the extracted and original watermarks ranging from 0.5308 to 0.9665 and PSNR values indicating impercept
A DWT based Dual Image Watermarking Technique for Authenticity and Watermark ...sipij
In this paper we propose a DWT based dual watermarking technique wherein both blind and non-blind algorithms are used for the copyright protection of the cover/host image and the watermark respectively. We use the concept of embedding two watermarks into the cover image by actually embedding only one, to authenticate the source image and protect the watermark simultaneously. Here the DWT coefficients of the primary watermark (logo) are modified using another smaller secondary binary image (sign) and the midfrequency coefficients of the cover/host image. Since the watermark has some features of host image embedded in it, the security is increased two-fold and it also protects the watermark from any misuse or copy attack. For this purpose a new pseudorandom generator based on the mathematical constant π has been developed and used successfully in various stages of the algorithm. We have also proposed a new approach of applying pseudo-randomness in selecting the watermark pixel values for embedding in the cover image. In all the existing techniques the randomness is incorporated in selecting the location to embed the watermark. This makes the embedding process more unpredictable. The cover image which is watermarked with the signed-logo is subjected to various attacks like cropping, rotation, JPEG compression, scaling and noising. From the results it has been found that it is very robust and has good invisibility as well.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
it is used for security purpose using two level dct and wavelet packet denoising .based on digital image processing.the software based on matlab.it is used for high security purpose.
DWT Based Audio Watermarking Schemes : A Comparative Study ijcisjournal
The main problem encountered during multimedia transmission is its protection against illegal distribution
and copying. One of the possible solutions for this is digital watermarking. Digital audio watermarking is
the technique of embedding watermark content to the audio signal to protect the owner copyrights. In this
paper, we used three wavelet transforms i.e. Discrete Wavelet Transform (DWT), Double Density DWT
(DDDWT) and Dual Tree DWT (DTDWT) for audio watermarking and the performance analysis of each
transform is presented. The key idea of the basic algorithm is to segment the audio signal into two parts,
one is for synchronization code insertion and other one is for watermark embedding. Initially, binary
watermark image is scrambled using chaotic technique to provide secrecy. By using QuantizationIndex
Modulation (QIM), this method works as a blind technique. The comparative analysis of the three methods
is made by conducting robustness and imperceptibility tests are conducted on five benchmark audio
signals.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document discusses a digital video watermarking technique using discrete cosine transform (DCT) and perceptual analysis. It proposes embedding a binary watermark in the DCT domain of video frames. A mathematical model is developed to insert a visible watermark into video frames in the DCT domain while considering characteristics of the human visual system to minimize perceptual quality impact. Experimental results show a watermarked video frame with the watermark logo embedded at different positions. The technique aims to provide copyright protection for digital video applications.
This document summarizes a research paper that proposes a novel video watermarking scheme using discrete wavelet transform (DWT) and principal component analysis (PCA). The scheme embeds a binary logo watermark into video frames for copyright protection. PCA is applied to blocks of two bands (LL-HH) resulting from DWT of video frames. The watermark is embedded into the principal components of LL and HH blocks at different levels. Combining DWT and PCA improves the watermarking performance by distributing the watermark bits over sub-bands, increasing robustness to attacks. The scheme provides imperceptible watermarking that is robust against various attacks such as geometric transformations and brightness/contrast adjustments.
A New Watermarking Algorithm Based on Image Scrambling and SVD in the Wavelet...IDES Editor
A new watermarking algorithm which is based on
image scrambling and SVD in the wavelet domain is discussed
in this paper. In the proposed algorithm, chaotic signals are
generated using logistic mapping and are used for scrambling
the original watermark. The initial values of logistic mapping
are taken as private keys. The covert image is decomposed
into four bands using integer wavelet transform; we apply
SVD to each band and embed the
Robust Digital Image Watermarking based on spread spectrum and convolutional ...IOSR Journals
This document proposes a robust digital image watermarking technique based on spread spectrum and convolutional coding. The watermark is first encoded using a convolutional encoder to improve robustness. It is then spread over the image using CDMA spread spectrum. During extraction, the watermark bits are decoded using a Viterbi algorithm decoder. Simulation results show the proposed method effectively resists common attacks like JPEG compression, noise, cropping and rotation. It performs better than directly embedding the watermark without convolutional coding, achieving higher PSNR values and correlation coefficients against various attacks.
The embedding of a digital signature, or tag data is carried out in the frequency domain. The
high frequency varieties are chosen by any LH and HL in the wavelet domain which are to be
applicable in DCT. Coefficients are changed mid-frequency DCT coefficients such transactions by a
low frequency of the watermark to be embedded. Watermark can be recovered from the video by
selecting a random watermark of any reference framework. The proposed techniques are more
secure, robust and are efficient due to the use of static DCT. Watermark techniques uses a bands HL
and LH for adding watermark where the movement does not impact the quality the extracted
watermark until if the video displays for different types of malware attacks.
In this work we have taken three video watermarking techniques i.e. BIT GET (spatial),
DWT, DCT and one video formats ie.MPEG video to perform a comparative analysis of different
techniques using single video formats, to obtain the best performing technique for video
watermarking. Such that to increase robustness of the video and decrease the embedding time
A New Technique to Digital Image Watermarking Using DWT for Real Time Applica...IJERA Editor
Digital watermarking is an essential technique to add hidden copyright notices or secret messages to digital audio, image, or image forms. In this paper we introduce a new approach for digital image watermarking for real time applications. We have successfully implemented the digital watermarking technique on digital images based on 2-level Discrete Wavelet Transform and compared the performance of the proposed method with Level-1 and Level-2 and Level-3 Discrete Wavelet Transform using the parameter peak signal to noise ratio. To make the watermark robust and to preserve visual significant information a 2-Level Discrete wavelet transform used as transformation domain for both secret image and original image. The watermark is embedded in the original image using Alpha blending technique and implemented using Matlab Simulink.
International journal of signal and image processing issues vol 2015 - no 1...sophiabelthome
This document discusses a method for embedding a binary watermark image into a digital video using a hybrid of three transforms: discrete cosine transform (DCT), discrete wavelet transform (DWT), and singular value decomposition (SVD). The method first applies DCT to frames of the video, then applies three-level DWT to the transformed frames. SVD is then applied to both the transformed video frames and the watermark image. The watermark is embedded by modifying coefficients of the video based on the SVD results. PSNR, MSE, and correlation are used to evaluate the quality and robustness of the watermarked video.
The proposed system implements an image watermarking technique that incorporates human visual system (HVS) models into watermark embedding. The watermarking is performed in the wavelet domain. The algorithm first calculates the coarseness of different subbands (HH, HL, LH) to select the subband with the highest coarseness for watermark embedding. It then embeds the watermark bits into the selected subband by modifying the least significant bits of coefficients based on their values. Experimental results on test images show the technique is robust, with average watermark extraction rates of 80-95% and high PSNR values, even after filtering.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and analyzes different digital watermarking techniques under various attacks. It compares the Least Significant Bit (LSB), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT) watermarking algorithms in terms of invisibility, distortion, and robustness. The LSB, DCT, and DWT watermark embedding and extraction procedures are described. Simulation results showed that the algorithms had good robustness against common image processing operations and were invisible with low distortion.
An Efficient Video Watermarking Using Color Histogram Analysis and Biplanes I...IJERA Editor
Video watermarking is a novel technology that has the ability to solve the problem of illegal digital video
manipulation and distribution. In video watermarking, the copyright bits are embedded into video bit streams.
This paper proposes an effective, robust and indiscernible video watermarking algorithm. A video can also
undergo several intentional attacks like frame dropping, averaging, cropping and median filtering and
unintentional attacks like addition of noise and compression which can compromise copyright information,
thereby denying the authentication. In this paper, the design and implementation of SVD and multiple bit plane
image based watermarking is proposed. The developed method embeds 8 bit-plane images, obtained from single
gray scale watermark image, into different frames of a video sequence. In this algorithm, some of the luminous
values in the video pictures are selected and divided into groups, and the watermark bits are embedded by
adjusting the relative relationship of the member in each group. A sufficient number of watermark bits will be
embedded into the video pictures without causing noticeable distortion. The watermark will be correctly
retrieved at the extraction stage, even after various types of video manipulation and other signal processing
attacks.
The document describes a video watermarking scheme based on discrete wavelet transform (DWT) and principal component analysis (PCA) for copyright protection. The scheme embeds a binary logo watermark into video frames by applying DWT to decompose frames into sub-bands, then applying block-based PCA on sub-blocks of low and high frequency sub-bands. The watermark is embedded into the principal components of the sub-blocks. Algorithms are provided for applying DWT, PCA transforms, and embedding and extracting the watermark. The scheme aims to provide imperceptibility, robustness against attacks, and ownership protection for digital video content.
Iaetsd wavelet transform based latency optimized image compression forIaetsd Iaetsd
This document discusses wavelet transform based image compression. It proposes a new discrete wavelet transform (DWT) architecture based on fast convolution that reduces hardware complexity and memory requirements while also decreasing the critical path delay. This allows the system to produce outputs in fewer clock cycles for improved efficiency. The proposed architecture is evaluated against existing designs and shown to achieve better performance in terms of reduced area and processing time.
This document provides an overview of digital watermarking techniques. It discusses how watermarking has evolved from earlier steganography methods and classifications of watermarking such as image, audio, and video watermarking. It also summarizes various watermarking techniques including spatial domain methods that directly modify pixel values, frequency domain methods that operate in transform domains like DCT and DWT, and spread spectrum techniques. Specific spatial and frequency domain techniques are described for image, audio, and video watermarking. The document concludes that watermarking continues to be an evolving topic with opportunities remaining to further develop fragile and semi-fragile techniques.
The document discusses DCT/IDCT concepts and applications. It provides an introduction to DCT and IDCT, explaining that they are used widely in video and audio compression. It describes the DCT and IDCT functions and how they work to transform signals between spatial and frequency domains. Examples of one-dimensional and two-dimensional DCT/IDCT equations are also given. Finally, common applications of DCT/IDCT compression techniques are listed, such as in DVD players, cable TV, graphics cards, and medical imaging systems.
Performance Analysis of Digital Watermarking Of Video in the Spatial Domainpaperpublications3
Abstract:In this paper, we have suggested the spatial domain method for the digital video watermarking for both visible and invisible watermarks. The methods are used for the copyright protection as well as proof of ownership. In this paper we first extracted the frames from the video and then used spatial domain characteristics of the frames where we directly worked on the pixel value of the frame according to the watermark and calculated different parameters.
Keywords:Digital video watermarking, copyright protection, spatial domain watermarking, Least Significant bit substitution.
Tchebichef image watermarking along the edge using YCoCg-R color space for co...IJECEIAES
The document summarizes a research paper that proposes a Tchebichef watermarking technique along image edges using the YCoCg-R color space for copyright protection. The technique embeds a scrambled watermark bit into selected blocks of the image that are transformed using Tchebichef moments. The blocks are selected based on having minimum human visual characteristic entropy. The locations of the matrix moments C(0,1), C(1,0), C(0,2) and C(2,0) are used for embedding to maintain image quality. An optimal threshold is determined to balance imperceptibility and robustness against JPEG compression attacks. The technique is tested on various color images and is shown to produce good
nternational Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Wavelet analysis involves representing a signal as a sum of wavelet functions of varying location and scale. Wavelet transforms allow for efficient video compression by removing spatial and temporal redundancies. Without compression, transmitting uncompressed video would require huge storage and bandwidth. Using wavelet compression, a day of video could be stored using the same space as an uncompressed minute. The discrete wavelet transform decomposes a signal into different frequency subbands, making it suitable for scalable and tolerant video compression standards like JPEG2000. Wavelet compression provides better quality at low bit rates compared to DCT techniques like JPEG.
In Digital era sharing of images have become very
common and raises the risk of using it for unethical and
fraudulent purposes with the help of manipulation tools. Digital
image watermarking is one way to protect the digital information
(text, images, audio, and video) from fraudulent manipulations.
Digital Image Watermarking is a process of implanting data in
the original image for authentication. In this paper we are
providing one such watermarking scheme for color images. The
proposed method is designed to be robust for common attacks
with the aid of redundant discrete wavelet transform (RDWT)
and discrete cosine transform (DCT) properties. After applying
two levels RDWT decomposition to the blue channel of cover
image, we apply DCT to HH_LL subband i.e. 2nd level
decomposed coefficient of HH band and to the watermark.
Divided the HH_LL sub band into 4x4 subblocks and DCT
coefficients of the last subblock of the cover image are replaced
with the DCT coefficients of watermark. Inverse DCT and
inverse RDWT is performed to get watermarked image. The
performance of the proposed technique is measured using the
parameters PSNR and NCC.
Hardware progress has enabled solutions which were historically computationally intractable. This is particularly true in video analysis. This technological advance has opened a new frontier of problems. Within this expanse, we have chosen the classic problem of depth inference from images. Specifically, given a sequence of images captured over time, we output depth maps corresponding one-to-one with the input sequence. As a spatiotemporal problem, we were motivated to model it with convolutions (spatial) andLSTMs (temporal). These are used in a U-Net encoder-decoder architecture. The results indicate some potential in such an approach, the process by which we came to this conclusion is detailed below
Digital Image Watermarking Techniques: A ReviewCSCJournals
Advancements in science and technology have introduced the need to protect data, authenticate data, integrate data, assert ownership, content labelling and security. Digital Watermarking schemes protect all forms of digital data. Digital Image Watermarking can be applied to gray scale, halftone, color, medical and 3D images. The process of watermarking can be broadly classified into three phases namely embedding, attacking, and decoding for typical scenarios. Some of the watermarking schemes adopted in the past include vector quantization, spread spectrum, SVD, DCT, DFT, etc. It was observed that the spread spectrum was more robust and it had also been applied for patenting. In spite of this, the method could not withstand high amplitude noise. Hence, later DCT, DFT and Wavelets were used. These schemes were not robust to collusion attacks. In this review, we have identified the embedding and detection schemes of the existing watermarks over the past decade and analyzed the robustness of each of these methods. The different parameters considered to analyze the performance of the existing watermarking schemes are also discussed. Research under watermarking is a great field of interest involving multimedia security, forensics, data authentication and digital rights protection. This paper will be useful for researchers to implement a robust watermarking scheme.
Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an ...ijceronline
The focus of the paper is to generate an advance algorithm of resource allocation and load balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine. In VM while processes are allocate they executes in queue , the first process get resources , other remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR) Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
A New Watermarking Algorithm Based on Image Scrambling and SVD in the Wavelet...IDES Editor
A new watermarking algorithm which is based on
image scrambling and SVD in the wavelet domain is discussed
in this paper. In the proposed algorithm, chaotic signals are
generated using logistic mapping and are used for scrambling
the original watermark. The initial values of logistic mapping
are taken as private keys. The covert image is decomposed
into four bands using integer wavelet transform; we apply
SVD to each band and embed the
Robust Digital Image Watermarking based on spread spectrum and convolutional ...IOSR Journals
This document proposes a robust digital image watermarking technique based on spread spectrum and convolutional coding. The watermark is first encoded using a convolutional encoder to improve robustness. It is then spread over the image using CDMA spread spectrum. During extraction, the watermark bits are decoded using a Viterbi algorithm decoder. Simulation results show the proposed method effectively resists common attacks like JPEG compression, noise, cropping and rotation. It performs better than directly embedding the watermark without convolutional coding, achieving higher PSNR values and correlation coefficients against various attacks.
The embedding of a digital signature, or tag data is carried out in the frequency domain. The
high frequency varieties are chosen by any LH and HL in the wavelet domain which are to be
applicable in DCT. Coefficients are changed mid-frequency DCT coefficients such transactions by a
low frequency of the watermark to be embedded. Watermark can be recovered from the video by
selecting a random watermark of any reference framework. The proposed techniques are more
secure, robust and are efficient due to the use of static DCT. Watermark techniques uses a bands HL
and LH for adding watermark where the movement does not impact the quality the extracted
watermark until if the video displays for different types of malware attacks.
In this work we have taken three video watermarking techniques i.e. BIT GET (spatial),
DWT, DCT and one video formats ie.MPEG video to perform a comparative analysis of different
techniques using single video formats, to obtain the best performing technique for video
watermarking. Such that to increase robustness of the video and decrease the embedding time
A New Technique to Digital Image Watermarking Using DWT for Real Time Applica...IJERA Editor
Digital watermarking is an essential technique to add hidden copyright notices or secret messages to digital audio, image, or image forms. In this paper we introduce a new approach for digital image watermarking for real time applications. We have successfully implemented the digital watermarking technique on digital images based on 2-level Discrete Wavelet Transform and compared the performance of the proposed method with Level-1 and Level-2 and Level-3 Discrete Wavelet Transform using the parameter peak signal to noise ratio. To make the watermark robust and to preserve visual significant information a 2-Level Discrete wavelet transform used as transformation domain for both secret image and original image. The watermark is embedded in the original image using Alpha blending technique and implemented using Matlab Simulink.
International journal of signal and image processing issues vol 2015 - no 1...sophiabelthome
This document discusses a method for embedding a binary watermark image into a digital video using a hybrid of three transforms: discrete cosine transform (DCT), discrete wavelet transform (DWT), and singular value decomposition (SVD). The method first applies DCT to frames of the video, then applies three-level DWT to the transformed frames. SVD is then applied to both the transformed video frames and the watermark image. The watermark is embedded by modifying coefficients of the video based on the SVD results. PSNR, MSE, and correlation are used to evaluate the quality and robustness of the watermarked video.
The proposed system implements an image watermarking technique that incorporates human visual system (HVS) models into watermark embedding. The watermarking is performed in the wavelet domain. The algorithm first calculates the coarseness of different subbands (HH, HL, LH) to select the subband with the highest coarseness for watermark embedding. It then embeds the watermark bits into the selected subband by modifying the least significant bits of coefficients based on their values. Experimental results on test images show the technique is robust, with average watermark extraction rates of 80-95% and high PSNR values, even after filtering.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and analyzes different digital watermarking techniques under various attacks. It compares the Least Significant Bit (LSB), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT) watermarking algorithms in terms of invisibility, distortion, and robustness. The LSB, DCT, and DWT watermark embedding and extraction procedures are described. Simulation results showed that the algorithms had good robustness against common image processing operations and were invisible with low distortion.
An Efficient Video Watermarking Using Color Histogram Analysis and Biplanes I...IJERA Editor
Video watermarking is a novel technology that has the ability to solve the problem of illegal digital video
manipulation and distribution. In video watermarking, the copyright bits are embedded into video bit streams.
This paper proposes an effective, robust and indiscernible video watermarking algorithm. A video can also
undergo several intentional attacks like frame dropping, averaging, cropping and median filtering and
unintentional attacks like addition of noise and compression which can compromise copyright information,
thereby denying the authentication. In this paper, the design and implementation of SVD and multiple bit plane
image based watermarking is proposed. The developed method embeds 8 bit-plane images, obtained from single
gray scale watermark image, into different frames of a video sequence. In this algorithm, some of the luminous
values in the video pictures are selected and divided into groups, and the watermark bits are embedded by
adjusting the relative relationship of the member in each group. A sufficient number of watermark bits will be
embedded into the video pictures without causing noticeable distortion. The watermark will be correctly
retrieved at the extraction stage, even after various types of video manipulation and other signal processing
attacks.
The document describes a video watermarking scheme based on discrete wavelet transform (DWT) and principal component analysis (PCA) for copyright protection. The scheme embeds a binary logo watermark into video frames by applying DWT to decompose frames into sub-bands, then applying block-based PCA on sub-blocks of low and high frequency sub-bands. The watermark is embedded into the principal components of the sub-blocks. Algorithms are provided for applying DWT, PCA transforms, and embedding and extracting the watermark. The scheme aims to provide imperceptibility, robustness against attacks, and ownership protection for digital video content.
Iaetsd wavelet transform based latency optimized image compression forIaetsd Iaetsd
This document discusses wavelet transform based image compression. It proposes a new discrete wavelet transform (DWT) architecture based on fast convolution that reduces hardware complexity and memory requirements while also decreasing the critical path delay. This allows the system to produce outputs in fewer clock cycles for improved efficiency. The proposed architecture is evaluated against existing designs and shown to achieve better performance in terms of reduced area and processing time.
This document provides an overview of digital watermarking techniques. It discusses how watermarking has evolved from earlier steganography methods and classifications of watermarking such as image, audio, and video watermarking. It also summarizes various watermarking techniques including spatial domain methods that directly modify pixel values, frequency domain methods that operate in transform domains like DCT and DWT, and spread spectrum techniques. Specific spatial and frequency domain techniques are described for image, audio, and video watermarking. The document concludes that watermarking continues to be an evolving topic with opportunities remaining to further develop fragile and semi-fragile techniques.
The document discusses DCT/IDCT concepts and applications. It provides an introduction to DCT and IDCT, explaining that they are used widely in video and audio compression. It describes the DCT and IDCT functions and how they work to transform signals between spatial and frequency domains. Examples of one-dimensional and two-dimensional DCT/IDCT equations are also given. Finally, common applications of DCT/IDCT compression techniques are listed, such as in DVD players, cable TV, graphics cards, and medical imaging systems.
Performance Analysis of Digital Watermarking Of Video in the Spatial Domainpaperpublications3
Abstract:In this paper, we have suggested the spatial domain method for the digital video watermarking for both visible and invisible watermarks. The methods are used for the copyright protection as well as proof of ownership. In this paper we first extracted the frames from the video and then used spatial domain characteristics of the frames where we directly worked on the pixel value of the frame according to the watermark and calculated different parameters.
Keywords:Digital video watermarking, copyright protection, spatial domain watermarking, Least Significant bit substitution.
Tchebichef image watermarking along the edge using YCoCg-R color space for co...IJECEIAES
The document summarizes a research paper that proposes a Tchebichef watermarking technique along image edges using the YCoCg-R color space for copyright protection. The technique embeds a scrambled watermark bit into selected blocks of the image that are transformed using Tchebichef moments. The blocks are selected based on having minimum human visual characteristic entropy. The locations of the matrix moments C(0,1), C(1,0), C(0,2) and C(2,0) are used for embedding to maintain image quality. An optimal threshold is determined to balance imperceptibility and robustness against JPEG compression attacks. The technique is tested on various color images and is shown to produce good
nternational Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Wavelet analysis involves representing a signal as a sum of wavelet functions of varying location and scale. Wavelet transforms allow for efficient video compression by removing spatial and temporal redundancies. Without compression, transmitting uncompressed video would require huge storage and bandwidth. Using wavelet compression, a day of video could be stored using the same space as an uncompressed minute. The discrete wavelet transform decomposes a signal into different frequency subbands, making it suitable for scalable and tolerant video compression standards like JPEG2000. Wavelet compression provides better quality at low bit rates compared to DCT techniques like JPEG.
In Digital era sharing of images have become very
common and raises the risk of using it for unethical and
fraudulent purposes with the help of manipulation tools. Digital
image watermarking is one way to protect the digital information
(text, images, audio, and video) from fraudulent manipulations.
Digital Image Watermarking is a process of implanting data in
the original image for authentication. In this paper we are
providing one such watermarking scheme for color images. The
proposed method is designed to be robust for common attacks
with the aid of redundant discrete wavelet transform (RDWT)
and discrete cosine transform (DCT) properties. After applying
two levels RDWT decomposition to the blue channel of cover
image, we apply DCT to HH_LL subband i.e. 2nd level
decomposed coefficient of HH band and to the watermark.
Divided the HH_LL sub band into 4x4 subblocks and DCT
coefficients of the last subblock of the cover image are replaced
with the DCT coefficients of watermark. Inverse DCT and
inverse RDWT is performed to get watermarked image. The
performance of the proposed technique is measured using the
parameters PSNR and NCC.
Hardware progress has enabled solutions which were historically computationally intractable. This is particularly true in video analysis. This technological advance has opened a new frontier of problems. Within this expanse, we have chosen the classic problem of depth inference from images. Specifically, given a sequence of images captured over time, we output depth maps corresponding one-to-one with the input sequence. As a spatiotemporal problem, we were motivated to model it with convolutions (spatial) andLSTMs (temporal). These are used in a U-Net encoder-decoder architecture. The results indicate some potential in such an approach, the process by which we came to this conclusion is detailed below
Digital Image Watermarking Techniques: A ReviewCSCJournals
Advancements in science and technology have introduced the need to protect data, authenticate data, integrate data, assert ownership, content labelling and security. Digital Watermarking schemes protect all forms of digital data. Digital Image Watermarking can be applied to gray scale, halftone, color, medical and 3D images. The process of watermarking can be broadly classified into three phases namely embedding, attacking, and decoding for typical scenarios. Some of the watermarking schemes adopted in the past include vector quantization, spread spectrum, SVD, DCT, DFT, etc. It was observed that the spread spectrum was more robust and it had also been applied for patenting. In spite of this, the method could not withstand high amplitude noise. Hence, later DCT, DFT and Wavelets were used. These schemes were not robust to collusion attacks. In this review, we have identified the embedding and detection schemes of the existing watermarks over the past decade and analyzed the robustness of each of these methods. The different parameters considered to analyze the performance of the existing watermarking schemes are also discussed. Research under watermarking is a great field of interest involving multimedia security, forensics, data authentication and digital rights protection. This paper will be useful for researchers to implement a robust watermarking scheme.
Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an ...ijceronline
The focus of the paper is to generate an advance algorithm of resource allocation and load balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine. In VM while processes are allocate they executes in queue , the first process get resources , other remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR) Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
A Study on Video Steganographic Techniquesijceronline
Data hiding techniques have taken important role with the rapid growth of intensive transfer of multimedia content and secret communications. The method of Steganography is used to share the data secretly and securely. It is the science of embedding secret information into the cover media with the modification to the cover image, which cannot be easily identified by human eyes. Steganography algorithms can be applied in audio, video and image file. Hiding secret information in video file is known as video steganography. Video Steganography means hiding a secret message that can be either a secret text message or an image within a larger one in such a way that just by looking at it, an unwanted person cannot detect the presence of any hidden message. For hiding secret information in the video, there are many Steganography techniques which are further explained in this paper along with some of the research works done in some fields under video steganography by some authors. The paper describes the progress in the field of video Steganography and intends to give the comparison between its different uses and techniques
Watermarking of JPEG2000 Compressed Images with Improved EncryptionEditor IJCATR
The need for copyright protection, ownership verification, and other issues for digital data are getting more and more interest nowadays. Among the solutions for these issues, digital watermarking techniques are used. A range of watermarking methods has been projected. Compression plays a foremost role in the design of watermarking algorithms. For a digital watermarking method to be effective, it is vital that an embedded watermark should be robust against compression. JPEG2000 is a new standard for image compression and transmission. JPEG2000 offers both lossy and lossless compression. The projected approach is used to execute a robust watermarking algorithm to watermark JPEG2000 compressed and encrypted images. For encryption it uses RC6 block cipher. The method embeds watermark in the compressed- encrypted domain and extraction is done in the decrypted domain. The proposal also preserves the confidentiality of substance as the embedding is done on encrypted data. On the whole 3 watermarking schemes are used: Spread Spectrum, Scalar Costa Scheme Quantization Index Modulation, and Rational Dither Modulation.
Study on groundwater quality in and around sipcot industrial complex, area cu...ijceronline
STATE INDUSTRIES PROMOTION CORPORATION OF TAMIL NADU(SIPCOT) cuddalore phase 1 has estabilished in 1984 at an extent of 518.79 acres. currently between 26 and 29 functional units are lie within phase1 of the industrial estates.At least 10 villages lie within or in the vicinity of the industrial complex. Till date no sites has been developed for secure storage of hazardous wastes generated by the industries in the estate. In absence of such facilities factories have dumped these wastes on neighbouring lands and in open pits. By the industries own admission,out of the 20 million litres of fresh water required by the companies, 18 million litres (90%) of the water is released back to their environment as toxic effluents.These poisons have leached into the ground water and contaminated the water resources of communities living around the factory. This study was carried out to asses the Quality of ground water in and around SIPCOT industrial complex in cuddalore district. The Quality was assessed in terms of physico chemical parameters.Ground water samples were collected from 30 locations in and around the study area and analyzed (APHA,1998) to know the present status of the Ground water Quality. The results were compared with standards prescribed by ISI 10500-91.It was found that the ground water was contaminated at few sampling locations.The remaining locations shows that the parameters are within the desirable limits and fit for drinking purpose
Rocker arms are part of the valve-actuating mechanism. A rocker arm is designed to pivot on a pivot
pin or shaft that is secured to a bracket. The bracket is mounted on the cylinder head. One end of a
rocker arm is in contact with the top of the valve stem, and the other end is actuated by the camshaft.
In installations where the camshaft is located below the cylinder head, the rocker arms
are actuated by pushrods. The lifters have rollers which are forced by the valve springs to follow the
profiles of the cams. Failure of rocker arm is a measure concern as it is one of the important
components of push rod IC engines.Present work finds the various stresses under extreme load
condition. For this we are modeling the arm using design software and the stressed regions are
found out usingAnsys software. Here in this thesis we are observing that by changing different
materials how the stresses are varying in the rocker arm under extreme load condition. And after
comparing results we are proposing best suitable material for the rocker arm under extreme load conditions.
Stress Analysis of a Centrifugal Supercharger Impeller Bladeijceronline
A supercharger is an air compressor that increases the pressure or density of air supplied to an internal combustion engine. This gives each intake cycle of the engine more oxygen, letting it burn
more fuel and do more work, thus increasing power. Power for the supercharger can be provided mechanically by means of a belt, gear, shaft, or chain connected to the engine's crankshaft. Superchargers are a type of forced induction system. They compress the air flowing into the engine.
The advantage of compressing the air is that it lets the engine squeeze more air into a cylinder, and more air means that more fuel can be added. Therefore, you get more power from each explosion in each cylinder. Here in this project we are designing the compressor wheel by using Pro-E and doing
analysis by using FEA package. An attempt has been made to investigate the effect of pressure and induced stresses on the blade. By identifying the true design feature, the extended service life and long term stability is assured. A
structural analysis has been carried out to investigate the stresses, strains and displacements of the blade. An attempt is also made to suggest the best material for an blade of a turbocharger by comparing the results obtained for different materials. Based on the results best material is recommended for the blade of a turbocharger
Strong (Weak) Triple Connected Domination Number of a Fuzzy Graphijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Development of a Cassava Starch Extraction Machineijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Engendering sustainable socio-spatial environment for tourism activities in t...ijceronline
The document summarizes a study that assessed the potential for knitting together the five states of South-Eastern Nigeria into a unified tourist destination of international significance. It identifies various tourism potentials across the region and evaluates the accessibility between state capitals. The study recommends adopting an Environmental Planning and Management process involving zonal, state, and local forums to coordinate development efforts and achieve a sustainable tourism environment across the region through public-private collaboration. This participatory approach aims to improve infrastructure like roads, airports, utilities and encourage private investment in tourism facilities.
The Myth of Softening behavior of the Cohesive Zone Model Exact derivation of...ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Secured Video Watermarking Based On DWTEditor IJMTER
Copy right protection and Claiming the digital rights is the major problem for the content
developers. Content like video, images, audio, etc., are prone to violation of protection under many
circumstances in the digital world. In this paper we propose a method that proves copyright of the
video by embedding watermarking on selected frames and ensures that the frames is not modified by
performing hashing and then modifying the frame based on Discrete Wavelet Transformations . This
method protects the video from many types of attacks like frame-edit
A Novel Digital Watermarking Technique for Video Copyright Protection cscpconf
This paper proposes a novel digital video watermarking technique that embeds both visible and invisible watermarks for improved copyright protection. The invisible watermark is embedded using discrete wavelet transform (DWT) in the high-high (HH) subband coefficients of video frames. The visible watermark is embedded partially in video frames depending on user input location. Experimental results on a gray-scale video show the embedded watermarks can be extracted after attacks like salt and pepper noise, Gaussian noise, and median filtering, with peak signal-to-noise ratios above 28 dB, demonstrating the technique's robustness.
Hybrid Approach for Robust Digital Video WatermarkingIJSRD
With the growing popularity of internet and digital media, digital watermarking techniques have been developed to protect the copyright of multimedia objects such as text, audio, video, etc. So, we have proposed a hybrid video watermarking technique which takes the advantages of different transforms like DWT, DCT, SVD and Arnold Transform, which enhances more security and provides robustness to the watermark. In this paper method, video is divided into several groups of frames, and one of the frames is selected where watermark will be embedded. Before embedding watermark in a selected frame it will be pre-processed with Arnold Transform which will provide security to it. The selected plane of video frame are decomposed using DWT and high frequency band HH, middle frequency bands LH, HL are transformed with DCT. The DCT coefficients are SVD transformed which are embedded with corresponding transformed coefficients of watermarks along with Arnold Transform. The embedded watermark is extracted with inverse process of embedding. The proposed algorithm is tested with various video sequences using MATLAB 2013a. The distortion quality of original image and watermark is controlled by the Peak Signal to Noise Ratio, Signal to Noise Ratio and Mean square error of the watermarked frame with original frame.
International Journal for Research in Applied Science & Engineeringpriyanka singh
This document describes a method for embedding a secret watermark image into a QR code image using discrete wavelet transform. The watermark embedding process involves:
1) Performing a two-level discrete wavelet transform on the QR code image to create sub-bands
2) Converting the watermark image (e.g. a logo) to a binary sequence and generating a pseudo-random sequence with a secret key
3) Embedding the watermark bits into one of the high frequency sub-bands by modifying pixel values
4) Performing inverse discrete wavelet transform to get the watermarked QR code image
The watermark can then be extracted without the original QR code by estimating the original pixel values and
Abstract: The increasing amount of applications using digital multimedia technologies has accentuated the need to provide copyright protection to multimedia data. This paper reviews one of the data hiding techniques - digital image watermarking. Through this paper we will explore some basic concepts of digital image watermarking techniques.Two different methods of digital image watermarking namely spatial domain watermarking and transform domain watermarking are briefly discussed in this paper. Furthermore, two different algorithms for a digital image watermarking have also been discussed. Also the comparision between the different algorithms,tests performed for the robustness and the applications of the digital image watermarking have also been discussed.
A Video Watermarking Scheme to Hinder Camcorder PiracyIOSR Journals
This document describes a video watermarking scheme to prevent camcorder piracy in movie theaters. The scheme embeds watermarks in video frames so that any compliant video player cannot play the video if recorded in a theater. The watermarking technique is robust to geometric distortions like rotation and scaling. It also prevents loss of quality from lossy compression formats. The scheme uses an integer wavelet transform for the watermark embedding and extraction processes, making it computationally efficient and lossless. Experimental results show the scheme can withstand various attacks like filtering, noise addition, resizing and rotation while accurately extracting the embedded watermarks.
Nowadays, digital watermarking has many
applications such as broadcast monitoring, owner identification,
proof of ownership, transaction tracking. Embedding a hidden
stream of bits in a file is called Digital Watermarking. This paper
introduces a LSB information hiding algorithm which can lift the
wavelet transform image. LSB based Steganography embeds the
hiding text message in least significant bit of the pixels. The
proposed method has good invisibility, robustness for a lot of
hidden attacks. As we think about the capacity lead us to think
about improved approach which can be achieved through
hardware implementation system by using Field Programmable
Gate Array (FPGA). In this paper hardware implementation of
digital watermarking system is proposed. MATLAB is used to
convert images into pixel-format files and to observe simulation
results. To implement this paper XPS & VB are needed. In XPS,
first select hardware & software components then by adding
source and header files & converting into bit streams and
download into FPGA, to obtain Stego image.
A Survey and Comparative Study on Video Watermarking Techniques with Referenc...IJERA Editor
This document summarizes and compares various video watermarking techniques with a focus on their applicability to mobile devices. It first defines key properties of video watermarking like imperceptibility, robustness, capacity, security, and computational cost. It then classifies watermarking techniques into spatial domain, frequency domain, and spatio-frequency domain methods. Popular techniques discussed include DCT, DWT, SVD. The document surveys several proposed video watermarking methods and compares them based on robustness, imperceptibility, payload, and time complexity. It also surveys methods specifically designed for mobile devices, discussing challenges like limited resources and evaluating algorithms based on their energy and performance on mobile.
Digital watermarking has been proposed as a solution to the problem of copyright protection of
multimedia documents in networked environments. There are two important issues that watermarking
algorithms need to address. First, watermarking schemes are required to provide trustworthy evidence for
protecting rightful ownership. Second, good watermarking schemes should satisfy the requirement of
robustness and resist distortions due to common image manipulations (such as filtering, compression,
etc.). In this paper, a watermarking algorithm is proposed based on the Discrete Wavelet Transform
(DWT), Fractional Fourier Transform (FrFT) and Singular value decomposition (SVD). Analysis and
experimental results show that the proposed watermarking method performs well in both security and
robustness.
A Review on Robust Digital Watermarking based on different Methods and its Ap...IJSRD
Digital Watermarking is the process of embedding data called watermark or signature or label or tag into a multimedia object (image or audio or video) so that the watermark can be extracted for ownership verification or authentication. A visible watermark is a secondary translucent image overlaid into the primary image and appears visible to a viewer on a careful inspection. The invisible watermark is embedded in such a way that the modification made to the pixel value is perceptually not noticed and it can be recovered only with an appropriate decoding mechanism. Digital watermarking is used to hide the information inside a signal, which cannot be easily extracted by the third party. Its widely used application is copyright protection of digital information. It is different from the encryption in the sense that it allows the user to access, view and interpret the signal but protect the ownership of the content. One of the current research areas is to protect digital watermark inside the information so that ownership of the information cannot be claimed by third party.
Digital video watermarking scheme using discrete wavelet transform and standa...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Digital watermarking is used for data authentication and copyright protection of digital media files.
Original host files required to recover the watermark operation in non-blind watermark system, which increases
system resources overhead. It also doubles memory capacity and communication band-width. This system uses a
robust video multiple watermarking technique which is based on image interlacing. In this system, a watermark
embedding/extracting is done by using three-level discrete wavelet transform (DWT), Arnold transform is used as
a watermark encryption/ decryption method, and gray image, color image, and video are used as watermarks.
Geometric, noising, format compression, and image processing attacks are used to test this system.
Keywords — Digital watermarking, Image interlacing, Arnold transform, Three level DWT, Authentication,
Security.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Hybrid DWT-SVD Method for Digital Video Watermarking Using Random Frame Sel...researchinventy
This document presents a hybrid DWT-SVD method for digital video watermarking using random frame selection. The proposed method embeds a watermark into randomly selected video frames by applying discrete wavelet transform and singular value decomposition. The blue channel of selected frames is used for watermark embedding in the mid-frequency DWT coefficients. Experimental results show the method provides good imperceptibility and robustness against various attacks like compression, cropping, noise addition, contrast changes and tampering. The normalization coefficient between original and extracted watermarks is used to evaluate the performance under different attacks.
Advance Digital Video Watermarking based on DWT-PCA for Copyright protectionIJERA Editor
This document presents a digital video watermarking technique based on discrete wavelet transform (DWT) and principal component analysis (PCA). It begins with an introduction to digital watermarking and an overview of spatial and transform domain watermarking methods. The document then describes DWT and PCA in more detail. It presents a watermarking scheme that uses DWT to decompose video frames into frequency subbands, and embeds a watermark into the principal components of the low frequency subband after applying PCA. Experimental results on a test video show the watermarked frames have no visible quality differences from the original and the watermark is robust to various attacks. The technique achieves imperceptibility measured by high peak signal-to-
A Survey on Video Watermarking Technologies based on Copyright Protection and...Editor IJCATR
Digital Watermark is class of marker or symbol secretly embedded in a multimedia signal such as Audio, Image or Video. It
is used to identify the ownership of the multimedia signal. Video watermarking is an emerging area for various applications like copy
control broadcast monitoring, video authentication, copyright protection and enhanced video coding. The main objective of this paper
is to present survey and comparisons of various available techniques on video watermarking based on copyright protection and
identification. Comparative study of various technologies gives the significant information about the PSNR, payload, quality factor
and also the various attacks used in video watermarking techniques. The best techniques in various scenarios are discussed in this
paper which will help the research scholars in field of video watermarking.
The document summarizes a 1-level discrete wavelet transform (DWT) image watermarking algorithm for embedding watermarks into RGB cover images. The algorithm applies 1-level DWT to decompose the RGB cover image and watermark into frequency subbands. It then inserts the watermark into the low-frequency approximation subband of the cover image using alpha blending. The watermarked image achieves good quality with high peak signal-to-noise ratio. The extracted watermark matches the original with a normalized correlation value close to 1, showing the algorithm efficiently detects the watermark. The algorithm is imperceptible and robust against various attacks like noise and filtering.
This document discusses and compares two digital image watermarking techniques: discrete cosine transform (DCT) domain watermarking and discrete wavelet transform (DWT) domain watermarking. It first provides background on digital watermarking and explains watermark embedding and extraction processes in both the spatial and frequency domains. It then proposes a specific DCT watermarking technique that embeds a watermark by modifying mid-band DCT coefficients of divided image blocks. A DWT watermarking technique is also proposed that embeds a watermark in the LH sub-band of the DWT. Finally, the document indicates that experimental results will be used to compare the robustness of the two techniques against various attacks.
This document discusses and compares two digital image watermarking techniques: discrete cosine transform (DCT) domain watermarking and discrete wavelet transform (DWT) domain watermarking. It first provides background on digital watermarking and explains watermark embedding and extraction processes in both the spatial and frequency domains. It then proposes a specific DCT watermarking technique that embeds a watermark by modifying mid-band DCT coefficients of divided image blocks. A DWT watermarking technique is also proposed that embeds a watermark in the low-high band of the DWT. Finally, the document indicates that experimental results will be used to compare the robustness of the two techniques against various attacks.
Design of digital video watermarking scheme using matlab simulinkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Similar to IJCER (www.ijceronline.com) International Journal of computational Engineering research (20)
Design of digital video watermarking scheme using matlab simulink
IJCER (www.ijceronline.com) International Journal of computational Engineering research
1. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
WATER MARKING SCHEME WITH HIGH CAPACITY CDMA
Dr K RameshBabu 1 , Vani.kasireddy 2
1
Professor, ECE Dept, hitam, jntuh, Hyderabad, AP, India
2
vani.kasireddy ,PG student,ECE dept,hitam,jntuh,Hyderabad,AP,India
Abstract:
In this paper, we propose a high capacity CDMA based watermarking scheme based on orthogonal pseudorandom
sequence subspace projection. We introduced a novel idea to eliminate the interference due to the correlation between the
host image and the code sequences in the watermark extraction phase, and therefore, it improve the robustness and message
capacity of the watermarking scheme. We give the implementation steps of the proposed scheme and test its performance
under different attack conditions by a series of experiments. Experimental results show higher robustness than the canonical
scheme under different attack conditions.
Keywords: CDMA, watermarking, high capacity, oval approach sub space projection, and wavelet transform.
1. Introduction
A. Digital watermarking life-cycle phases
Fig 1.1 Digital watermarking life-cycle phases
Then the watermarked digital signal is transmitted or stored, usually transmitted to another person. If this person
makes a modification, this is called an attack. While the modification may not be malicious, the term attack arises from
copyright protection application, where pirates attempt to remove the digital watermark through modification. There are
many possible modifications, for example, lossy compression of the data (in which resolution is diminished), cropping an
image or video or intentionally adding noise. Detection (often called extraction) is an algorithm which is applied to the
attacked signal to attempt to extract the watermark from it. If the signal was unmodified during transmission, then the
watermark still is present and it may be extracted. In robust digital watermarking applications, the extraction algorithm
should be able to produce the watermark correctly, even if the modifications were strong. In fragile digital watermarking,
the extraction algorithm should fail if any change is made to the signal.
B Digital Watermark: Also referred to as simply watermark, a pattern of bits inserted into a digital image, audio, video or
text file that identifies the file's copyright information (author, rights, etc.). The name comes from the faintly visible
watermarks imprinted on stationary that identify the manufacturer of the stationery. The purpose of digital watermarks is to
provide copyright protection for intellectual property that's in digital format.
C. General Framework for Digital Watermarking: Digital watermarking is similar to watermarking physical objects
except that the watermarking technique is used for digital content instead of physical objects. In digital watermarking a low-
energy signal is imperceptibly embedded in another signal. The low energy signal is called watermark and it depicts some
metadata, like security or rights information about the main signal. The main signal in which the watermark is embedded is
referred to as cover signal since it covers the watermark. The cover signal is generally a still image, audio clip, video
sequence or a text document in digital format.
Issn 2250-3005(online) November| 2012 Page 112
2. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
Fig 1.3: watermark embedded and a watermark detector
D. Digital Watermarking System: The digital watermarking system essentially consists of a watermark embedded and a
watermark detector (see Figure). The watermark embedded inserts a watermark onto the cover signal and the watermark
detector detects the presence of watermark signal. Note that an entity called watermark key is used during the process of
embedding and detecting watermarks. The watermark key has a one-to-one correspondence with Watermark signal (i.e., a
unique watermark key exists for every watermark signal). The watermark key is private and known to only authorized
parties and it ensures that only authorized parties can detect the watermark. Further, note that the communication channel
can be noisy and hostile (i.e., prone to security attacks) and hence the digital watermarking techniques should be resilient to
both noise and security attacks.A watermark is an identifying feature, like a company logo, which can be used to
provide protection of some “cover” data. A watermark may be either visible i.e. perceptible or invisible i.e. Imperceptible both
of which offer specific advantages when it comes to protecting data. Watermarks may be used to prove ownership of data, and
also as an attempt to enforce copyright restrictions.
Types of watermarking:
i) Visible watermarking.
ii) Invisible watermarking
iii) Watermarking applications:
2. Cdma Watermarking:
2.1. Development of CDMA water marking model
There are several prior references to CDMA watermarking of digital video. Hartung and Girod‟s work is notable in
recognizing CDMA as a viable choice for video watermarking. Their approach parses the video into a linear stream of
individual pixel elements. A watermark represented by a binary pattern is then expanded by an m-sequence and added pixel-
by-pixel to the uncompressed video. Watermark recovery is done by matched filtering. In this paper we build upon the work
reported in by developing a more complete model for CDMA-based video watermarking in a multi-user/multiple media
environment. In particular, instead of linearizng the video as a 1-D pixel stream, we model the video as a bit plane stream;
the 2D counterpart of bit stream used in (1). By closely following the conventional CDMA model, it is possible to address a
variety of watermark removal/destruction attempts that go beyond random noise attacks. For example, any spread spectrum
watermarking that relies on m-sequences is extremely sensitive to timing errors. Frame drops, intentional or otherwise,
destroy the delicate pattern of an m-sequence and can seriously challenge watermark identification.We model digital video
as a function in time and space represented by I (x, y, t). I(x, y, t) can then be sequenced along the time axis as bit
planes:Where i(.) is the nth bit plane of the jth frame positioned at t = jTf + nTb . Tf and Tb are frame length and bit plane
spacing respectively and are related by Tf = bTb where b is the number of bit planes per frame.
b
I ( x, y , t ) i( x, y, t ( jT
j n 0
f nTb )) Eq.3
Two questions arise at this point, 1): how is a watermark defined? and 2): where in the bit plane stream is it inserted. We
define the watermark by a bit plane, w(x, y), spatial dimensions of which match that of the video frames. The content of the
watermark plane can be selected to suite varied requirements. It can contain a graphical seal, textual information about the
source or any other data deemed appropriate for watermarking. In the context of CDMA, w(x, y) can be thought of as the
message. This message is then spread using a 2D m-sequence or m-frames f(x, y, t). To generate m-frames, a one
dimensional m sequence is rearranged in a 2D pattern. Depending on the period of the m-sequence and the size of each
video frame, the 1D to 2D conversion may span up to k frames and will repeat afterwards. Spreading of the “message”, i.e.
the watermark w(x, y) is now defined by a periodic frame sequence given
k 1
by wss w( x, y ) j ( x , y , tj ) ……. Eq. 4
j 0
Issn 2250-3005(online) November| 2012 Page 113
3. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
Where f j (x, y, t j ) is f j positioned at yet to be determined locations t = t j . wss must now be aligned with and
inserted into video bit plane stream in (3). The embedding algorithm works as follows. In every frame the bit plane at t = t j
is tagged then removed and replaced by f j (x, y, t j ). The question now is which bit planes are tagged and in what order? It
is safe to assume that in most cases the LSB plane bit distribution is random and can be safely replaced by the watermark.
However, LSB plane is vulnerable to noise and other disturbances but bit planes can be used to embed a watermark with
small to negligible effect on quality. In one example, watermark placement in one of 4 lower bit planes did not significantly
impact video quality.In order to embed wss in the video, we define a separate multilevel sequence v and use v( j) as pointer
to the jth bit plane position. There are many ways to create v. One simple method is to start with a binary m-sequence u and
add 3 cyclically shifted versions. Let u = (uo,u1,L,up-1) be an m-sequence of period p. Define D as an operator that
cyclically shifts the elements of u to the left D(u) = (u1,L,up-1,uo ). We define v by v = u + D (u) + D2 (u) where Dk is the
kth cyclic shift of u. The new sequence now has two key properties, 1): it is still periodic with period p and 2): it is a 4
valued sequence taking on amplitudes in the range {0, 1… 3}. The significance of 4 values is that the watermark will be
limited to 4 lower bit plane positions. This number can clearly change. We can now align wss in (4) with the timeline
k 1
defined in (3) wss w( x, y ) ( x, y , v ( j )Tb ) ………………………… Eq. 5
j 0
wss is now a spread spectrum version of the watermark at pseudorandom locations determined by v( j). The second task is
accomplished by using v (j) Tb as pointers to the candidate bit planes where the watermark must be inserted. In order to take
the last step, the designated bit planes must be removed and replaced by the corresponding elements of wss . The formalism
to achieve this goal is through the use of a gate function defined by
0
gate(t v( j )Tb) for t=v(j)Tb 0 t Tc ………… Eq .6
1
Multiplying video bit plane stream in (3) by the gate function above removes the bit plane at v( j). The spread watermark bit
plane stream in (5) is positioned such that the individual planes correspond exactly to the planes just nulled by the gate
function. Putting it all together, CDMA watermarked video can be written as
b1
Iwm( x, y, t ) i( x, y, jTf nTb) gate(t jTf v(n)Tb) w( x, y)j ( x, y, jTf v(n)Tb)
j n 0
j k j Eq.7
3. Wavelet Transforms:
It provides the time-frequency representation. often times a particular spectral component occurring at any instant
can be of particular interest. In these cases it may be very beneficial to know the time intervals these particular spectral
components occur. For example, in EEGs, the latency of an event-related potential is of particular interest. Wavelet
transform is capable of providing the time and frequency information simultaneously, hence giving a time-frequency
representation of the signal.
3.1.The Continuous Wavelet Transform: The continuous wavelet transform was developed as alternative approaches to
the short time Fourier transform to overcome the resolution problem. The wavelet analysis is done in a similar way to the
STFT analysis, in the sense that the signal is multiplied with a function, {it the wavelet}, similar to the window function in
the STFT, and the transform is computed separately for different segments of the time-domain signal. However, there are
two main differences between the STFT and the CWT: 1. The Fourier transforms of the windowed signals are not taken, and
therefore single peak will be seen corresponding to a sinusoid, i.e., negative frequencies are not computed. 2. The width of
the window is changed as the transform is computed for every single spectral component, which is probably the most
significant characteristic of the wavelet transform. The continuous wavelet transform is defined as follows
…………………. Eq 3.1
As seen above variables, tau and s, the translation and scale parameters, respectively. psi(t) is the transforming function,
and it is called the mother wavelet .
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The scale
The parameter scale in the wavelet analysis is similar to the scale used in maps. As in the case of maps, high scales
correspond to a non-detailed global view (of the signal), and low scales correspond to a detailed view. Similarly, in terms of
frequency, low frequencies (high scales) correspond to a global information of a signal (that usually spans the entire signal),
whereas high frequencies (low scales) correspond to a detailed information of a hidden pattern in the signal (that usually
lasts a relatively short time). Cosine signals corresponding to various scales are given as examples in the following figure.
Fig 3.1: Cosine signals corresponding to various scales
Fortunately in practical applications, low scales (high frequencies) do not last for the entire duration of the signal, unlike
those shown in the figure, but they usually appear from time to time as short bursts, or spikes. High scales (low frequencies)
usually last for the entire duration of the signal. Scaling, as a mathematical operation, either dilates or compresses a signal.
Larger scales correspond to dilated (or stretched out) signals and small scales correspond to compressed signals. All of the
signals given in the figure are derived from the same cosine signal, i.e., they are dilated or compressed versions of the same
function. In the above figure, s=0.05 is the smallest scale, and s=1 is the largest scale. In terms of mathematical functions, if
f(t) is a given function f(st) corresponds to a contracted (compressed) version of f(t) if s > 1 and to an expanded (dilated)
version of f(t) if s < 1 . However, in the definition of the wavelet transform, the scaling term is used in the denominator, and
therefore, the opposite of the above statements holds, i.e., scales s > 1 dilates the signals whereas scales s < 1 , compresses
the signal. This interpretation of scale will be used throughout this text.
3.2 Computations of Cwt
Interpretation of the above equation will be explained in this section. Let x(t) is the signal to be analyzed. The
mother wavelet is chosen to serve as a prototype for all windows in the process. All the windows that are used are the
dilated (or compressed) and shifted versions of the mother wavelet. There are a number of functions that are used for this
purpose. The Morlet wavelet and the Mexican hat function are two candidates, and theyare used for the wavelet analysis.
Fig 3.2: (CWT) of this signal Fig 3.3 CWT signal with high frequencies
Note that in Figure 3.2 that smaller scales correspond to higher frequencies, i.e., frequency decreases as scale increases,
therefore, that portion of the graph with scales around zero, actually correspond to highest frequencies in the analysis, and
that with high scales correspond to lowest frequencies. Remember that the signal had 30 Hz (highest frequency) components
first, and this appears at the lowest scale at translations of 0 to 30. Then comes the 20 Hz component, second highest
frequency, and so on. The 5 Hz component appears at the end of the translation axis (as expected), and at higher scales
(lower frequencies) again as expected. Now, recall these resolution properties: Unlike the STFT which has a constant
resolution at all times and frequencies, the WT has a good time and poor frequency resolution at high frequencies, and good
frequency and poor time resolution at low frequencies. Figure 3.3 shows the same WT in Figure 3.2 from another angle to
better illustrate the resolution properties: In Figure 3.3, lower scales (higher frequencies) have better scale resolution
(narrower in scale, which means that it is less ambiguous what the exact value of the scale) which correspond to poorer
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frequency resolution :Similarly, higher scales have scale frequency resolution (wider support in scale, which means it is
more ambitious what the exact value of the scale is) , which correspond to better frequency resolution of lower
frequencies.The axes in Figure 3.2and 3.3 are normalized and should be evaluated accordingly. Roughly speaking the 100
points in the translation axis correspond to 1000 ms, and the 150 points on the scale axis correspond to a frequency band of
40 Hz (the numbers on the translation and scale axis do not correspond to seconds and Hz, respectively , they are just the
number of samples in the computation).
4. Discrete Wavelet Transforms
4.1.Need of Discrete Wavelet Transform
Although the DWT enables the computation of the continuous wavelet transform by computers, it is not a true
discrete transform. As a matter of fact, the wavelet series is simply a sampled version of the CWT, and the information it
provides is highly redundant as far as the reconstruction of the signal is concerned. This redundancy, on the other hand,
requires a significant amount of computation time and resources. The discrete wavelet transform (DWT), on the other hand,
provides sufficient information both for analysis and synthesis of the original signal, with a significant reduction in the
computation time.The DWT is considerably easier to implement when compared to the CWT.
4.2. Discrete wavelet transforms (DWT): The foundations of the DWT go back to 1976 when Croiser, Esteban, and
Galand devised a technique to decompose discrete time signals. Crochiere, Weber, and Flanagan did a similar work on
coding of speech signals in the same year. They named their analysis scheme as sub band coding. In 1983, Burt defined a
technique very similar to sub band coding and named it pyramidal coding which is also known as multi resolution analysis.
Later in 1989, Vetterli and Le Gall made some improvements to the sub band coding scheme, removing the existing
redundancy in the pyramidal coding scheme. Sub band coding is explained below. A detailed coverage of the discrete
wavelet transform and theory of multi resolution analysis can be found in a number of articles and books that are available
on this topic, and it is beyond the scope of this tutorial.
4.2.1The Sub band Coding and the Multi resolution Analysis: The frequencies that are most prominent in the original
signal will appear as high amplitudes in that region of the DWT signal that includes those particular frequencies. The
difference of this transform from the Fourier transform is that the time localization of these frequencies will not be lost
Fig 4.1. Bandwidth of the signal at every level
However, the time localization will have a resolution that depends on which level they appear. If the main
information of the signal lies in the high frequencies, as happens most often, the time localization of these frequencies will
be more precise, since they are characterized by more number of samples. If the main information lies only at very low
frequencies, the time localization will not be very precise, since few samples are used to express signal at these frequencies.
This procedure in effect offers a good time resolution at high frequencies, and good frequency resolution at low frequencies.
Most practical signals encountered are of this type. The frequency bands that are not very prominent in the original signal
will have very low amplitudes, and that part of the DWT signal can be discarded without any major loss of information,
allowing data reduction. Figure 4.2 illustrates an example of how DWT signals look like and how data reduction is
provided. Figure 4.2a shows a typical 512-sample signal that is normalized to unit amplitude. The horizontal axis is the
number of samples, whereas the vertical axis is the normalized amplitude. Figure 4.2b shows the 8 level DWT of the signal
in Figure 4.2a. The last 256 samples in this signal correspond to the highest frequency band in the signal, the previous 128
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samples correspond to the second highest frequency band and so on. It should be noted that only the first 64 samples, which
correspond to lower frequencies of the analysis, carry relevant information and the rest of this signal has virtually no
information. Therefore, all but the first 64 samples can be discarded without any loss of information. This is how DWT
provides a very effective data reduction scheme.
Fig 4.2 a,b Example of a DWT
One important property of the discrete wavelet transform is the relationship between the impulse responses of the high pass
and low pass filters. The high pass and low pass filters are not independent of each other, and they are related by
g[ L 1 n] (1)n.h[n]
Where g[n] is the high pass, h[n] is the low pass filter, and L is the filter length (in number of points). Note that the two
filters are odd index alternated reversed versions of each other. Low pass to high pass conversion is provided by the (-1)n
term. Filters satisfying this condition are commonly used in signal processing, and they are known as the Quadrature Mirror
Filters (QMF). The two filtering and sub sampling operations can be expressed by
yhigh[ k ] x[n]. g[ n 2k ]
n
ylow[k ] x[n].h[ n 2k ]
n
The reconstruction in this case is very easy since half band filters form orthonormal bases. The above procedure is followed
in reverse order for the reconstruction. The signals at every level are up sampled by two, passed through the synthesis filters
g‟[n], and h‟[n] (high pass and low pass, respectively), and then added. The interesting point here is that the analysis and
synthesis filters are identical to each other, except for a time reversal. Therefore, the reconstruction formula becomes (for
each layer) x[n] (y
k
[k ]. g[n 2k ]) ( ylow[k ].h[n 2k ])
high
However, if the filters are not ideal half band, then perfect reconstruction cannot be achieved. Although it is not
possible to realize ideal filters, under certain conditions it is possible to find filters that provide perfect reconstruction. The
most famous ones are the ones developed by Ingrid Daubechies, and they are known as Daubechies‟ wavelets. Note that due
to successive sub sampling by 2, the signal length must be a power of 2, or at least a multiple of power of 2, in order this
scheme to be efficient. The length of the signal determines the number of levels that the signal can be decomposed to. For
example, if the signal length is 1024, ten levels of decomposition are possible. Interpreting the DWT coefficients can
sometimes be rather difficult because the way DWT coefficients are presented is rather peculiar. To make a real long story
real short, DWT coefficients of each level are concatenated, starting with the last level. An example is in order to make this
concept clear:Suppose we have a 256-sample long signal sampled at 10 MHZ and we wish to obtain its DWT coefficients.
Since the signal is sampled at 10 MHz, the highest frequency component that exists in the signal is 5 MHz. At the first level,
the signal is passed through the low pass filter h[n], and the high pass filter g[n], the outputs of which are sub sampled by
two. The high pass filter output is the first level DWT coefficients. There are 128 of them, and they represent the signal in
the [2.5 5] MHz range. These 128 samples are the last 128 samples plotted. The low pass filter output, which also has 128
samples, but spanning the frequency band of [0 2.5] MHz, are further decomposed by passing them through the same h[n]
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And g[n]. The output of the second high pass filter is the level 2 DWT coefficients and these 64 samples precede
the 128 level 1 coefficients in the plot. The output of the second low pass filter is further decomposed, once again by passing
it through the filters h[n] and g[n]. The output of the third high pass filter is the level 3 DWT coefficients. These 32 samples
precede the level 2 DWT coefficients in the plot. The procedure continues until only 1 DWT coefficient can be computed at
level 9. This one coefficient is the first to be plotted in the DWT plot. This is followed by 2 level 8 coefficients, 4 level 7
coefficients, 8 level 6 coefficients, 16 level 5 coefficients, 32 level 4 coefficients, 64 level 3 coefficients, 128 level 2
coefficients and finally 256 level 1 coefficients. Note that less and less number of samples is used at lower frequencies,
therefore, the time resolution decreases as frequency decreases, but since the frequency interval also decreases at low
frequencies, the frequency resolution increases. Obviously, the first few coefficients would not carry a whole lot of
information, simply due to greatly reduced time resolution. To illustrate this richly bizarre DWT representation let us take a
look at a real world signal. Our original signal is a 256-sample long ultrasonic signal, which was sampled at 25 MHz. This
signal was originally generated by using a 2.25 MHz transducer; therefore the main spectral component of the signal is at
2.25 MHz. The last 128 samples correspond to [6.25 12.5] MHz range. As seen from the plot, no information is available
here; hence these samples can be discarded without any loss of information. The preceding 64 samples represent the signal
in the [3.12 6.25] MHz range, which also does not carry any significant information. The little glitches probably correspond
to the high frequency noise in the signal. The preceding 32 samples represent the signal in the [1.5 3.1] MHz range.As you
can see, the majority of the signal‟s energy is focused in these 32 samples, as we expected to see. The previous 16 samples
correspond to [0.75 1.5] MHz and the peaks that are seen at this level probably represent the lower frequency envelope of
the signal. The previous samples probably do not carry any other significant information. It is safe to say that we can get by
with the 3rd and 4th level coefficients, that are we can represent this 256 sample long signal with 16+32=48 samples, a
significant data reduction which would make your computer quite happy.
One area that has benefited the most from this particular property of the wavelet transforms is image processing. As
you may well know, images, particularly high-resolution images, claim a lot of disk space. As a matter of fact, if this tutorial
is taking a long time to download, that is mostly because of the images. DWT can be used to reduce the image size without
losing much of the resolution. Here is how: For a given image, you can compute the DWT of, say each row, and discard all
values in the DWT that are less then a certain threshold. We then save only those DWT coefficients that are above the
threshold for each row, and when we need to reconstruct the original image, we simply pad each row with as many zeros as
the number of discarded coefficients, and use the inverse DWT to reconstruct each row of the original image. We can also
analyze the image at different frequency bands, and reconstruct the original image by using only the coefficients that are of
a particular band. I will try to put sample images hopefully soon, to illustrate this point.Another issue that is receiving more
and more attention is carrying out the decomposition (sub band coding) not only on the low pass side but on both sides. In
other words, zooming into both low and high frequency bands of the signal separately. This can be visualized as having both
sides of the tree structure of Figure 4.1. What result is what is known as the wavelet packages we will not discuss wavelet
packages in this here, since it is beyond the scope of this tutorial. Anyone who is interested in wavelet packages or more
information on DWT can find this information in any of the numerous texts available in the market.And this concludes our
mini series of wavelet tutorial. If I could be of any assistance to anyone struggling to understand the wavelets, I would
consider the time and the effort that went into this tutorial well spent. I would like to remind that this tutorial is neither a
complete nor a through coverage of the wavelet transforms. It is merely an overview of the concept of wavelets and it was
intended to serve as a first reference for those who find the available texts on wavelets rather complicated. There might be
many structural and/or technical mistakes, and I would appreciate if you could point those out to me. Your feedback is of
utmost importance for the success of this tutorial.
5 Water Marking Schemes
5.1. the Channel Model of Canonical CDMA based Watermarking Schemes
Since discrete wavelet transform (DWT) is believed to more accurately models aspects of the Human Visual
System (HVS) as compared to the FFT or DCT, watermark information are embedded in the wavelet domain for many
CDMA based watermarking schemes. The host image is first transformed by orthogonal or bi orthogonal wavelets to obtain
several sub band images (each sub band image consists of wavelet coefficients). Then some of them are selected for
watermark embedding. Suppose sub band image I is chosen for watermark embedding and the message is represented in
binary form b (b1, b2,......, L) where bi 0,1 we first transform
b
b into a binary polar sequence m of 1,1 by the following formula
mi 1 2bi, i=1, 2, …...L. (1)
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According to the CDMA principles, the message m is encoded by L uncorrelated pseudo sequences s1, s 2,......... sL
generated by a secrete key, such as m sequences, gold sequences, etc.. Since it is possible to make them orthogonal with
each other, we simply assume that they are orthogonal unit vectors, i.e,
0, i j,
si, sj i, j=1,2,…….,L. (2)
1, i j.
Where, <•, •> denotes inner product operation. The pseudorandom noise pattern W is obtained as follows
L
W ms ,
i 1
i i (3)
This submerges the watermark message. Then the pseudorandom noise pattern W is embedded into the sub band image I as
follows
Iw I W , (4)
Where is a positive number, called the water mark strength parameter. Then an inverse wavelet transform is performed
to obtain the water marked image.
In the water marked extracting phase, the water marked image is transformed by the same wavelet transform that is
used in the watermark embedding phase to obtain the sub band image that contains the watermark message, i.e.,
ˆ
Iw I W n, (5)
Where n is the distortion due to attacks or simply quantization errors if no other attack is performed. Then the orthogonal
pseudo sequences s1, s 2,......... sL are generated using the key, and the inner product between each and is
computed:
ˆ
si, Iw si, I mi si, n (6)
The canonical CDMA based methods decide the sign of m i by computing the inner product on the left most of (6), i.e.,
1 if
, ˆ
si , Iw 0,
ˆ
mi (7)
1 otherwise.
,
ˆ ˆ
Where mi denotes the estimated value of mi This equivalent to neglecting of correlation between si and the host image I,
and the host image I , and the correlation between si and the attack distortion n . When the message size is small, we can
take a large watermark strength parameter λ, so we have no problem to neglect those small values. But when the message
size is large, problem occurs. For the convenience of analysis, we ignore the third term in (6) at present. Then we have
ˆ
si, Iw si, I mi . (8)
As the message size increases, the watermark strength parameter λ becomes smaller and smaller in order to keep the
imperceptibility. So the influence of the host image‟s contents becomes more and more prominent as the message size
increases. Experimental results also confirm this fact. So we must find a way to eliminate or reduce the interference of the
host image so that we can improve the robustness of the CDMA watermarking scheme considerably.
5.2. High Capacity CDMA Watermarking Scheme: In the previous subsection we have analyzed, the influence of the
host image‟s content to the robustness of the canonical CDMA watermarking schemes. In order to eliminate this influence,
we project the host image onto the linear subspace S generated by the orthogonal pseudorandom sequences, i.e.
L
Ps ( I ) i 1
si , I si. (9)
If we keep the projection coefficients i
c si, I : i 1,2.......,
L as a secret key, then we can subtract Ps (I ) from the
watermarked sub band image I, Before watermark extraction, therefore, we can decide the sign of ˆ
mi by computing
ˆ
si , Iw Ps ( I ) si , I W Ps ( I ) si , W mi , (10)
Which is not affected by the host image‟s contents, and therefore, provides a more robust way for CDMA based
watermarking.
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5.2.1.Watermark Embedding Process:The watermark embedding process of the proposed high capacity CDMA scheme is
the same as the canonical one except for a preprocessing step of calculating the projection
coefficients i si, I : i 1,2.......,L, which should be kept as a key for watermark extraction. Fig. 1 gives the flow chart of
c
the watermark embedding process.
Here we give the watermark embedding steps:
Step 1: decompose the host image into sub band images using orthogonal or bi orthogonal discrete wavelet transform
(DWT), and chose one or several sub band images I for watermark embedding;
Step2: generate the orthogonal pseudorandom sequences s1, s 2,......... sL using the secret key (key1);
Step3: project the sub band images I onto the linear subspace S generated by the orthogonal pseudo sequences, and keep the
projection coefficients i si, I : i 1,2.......,L as the second secret key (key2) which will be used in the watermark
c
extraction phase;
Step4: encode the watermark information using formula (1) and (3) to get the pseudorandom noise pattern W;
Step5: embed the pseudorandom noise pattern W into the sub band image I using formula (4);
Step6: perform inverse discrete wavelet transform (IDWT) to obtain the watermarked image.
Fig 5.1 the watermark embedding process of the proposed scheme.
Key1 is the key used to generate the orthogonal pseudo sequences; PSG is the pseudo sequence generator; PS is the
orthogonal projection operator; Key2 is generated by the projection operator, which consists of the projection coefficients,
will be used in the watermark extraction phase; DWT denotes the discrete wavelet transform; WE denotes watermark
embedding; IDWT denotes inverse wavelet transform.
Watermark Extraction Process: Now we give the watermark extraction steps:
Step1: decompose the received image into sub band images using the same wavelet transform as the one used in the
watermark embedding phase, and choose the corresponding sub band images Iˆw for watermark extraction;
Step2: generate the orthogonal pseudorandom sequences s1, s 2,......... sL using the secrete key (key1);
Step3: eliminate the projection component from Iˆw by
~ ˆ ˆ
L
I w Iw Ps ( I ) Iw cjsj (11)
j 1
Where Ci are the projection coefficients kept in the second secret key (key2);
Step4: extract the embedded message m (m1, m2,......., L) by correlation detection
m
~
ˆ
mi
1, if si , I w 0, (12)
,otherwise
1
Step5: transform the extracted message m (m1, m2,......., L) into the original watermark b (b1, b2,......, L) by
m b
bi (1 mi ) 2, i 1,2,.......
L (13)
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6. Performance Test
We have performed a series of experiments to test the robustness of the proposed scheme. Seven 512x512
grayscale images (a. airplane, b. baboon, c. Barbara, d. boats, e. gold hill, f.Lena, g. pepper.) are chosen as test images. The
watermarks are binary sequences of different size. The pseudorandom sequences we used are generated by pseudorandom
number generators and we orthogonalize them by Cholesky decomposition method. Of course other choices of pseudo
sequences such as m sequences, gold sequences may be more suitable for watermarking; we will test them in the future.
A. Capacity VS Bit Error Rate (BER)
The first test we have performed is to test the relationship between message capacity and the bit error rate of the
extracted watermark for both the canonical and newly proposed schemes. The bit error rate (BER) is calculated by the
following formula:
1 m n
BER W (i, j ) EXW (i, j )
mn i 1 j 1
(14)
Fig 5.2 the relationship between message capacity and the bit error rate
Where W denotes the original watermark, ExW denotes the extracted watermark. In this test, we embed the
watermarks into the lower resolution approximation image (LL) of the 2- level biorthogonal discrete wavelet decomposition
of the test image using both canonical and the newly proposed CDMA based schemes, no attack is performed on the
watermarked image except for quantization errors. Then extract watermarks from the watermarked image using
corresponding watermark extraction schemes and compare the extracted watermark with the original one. The watermark
size (number of information bits) vary from 16 to 1015, we have chosen 11 discrete values for our test. For each watermark
size value, we perform the watermark embedding and extracting process on all 7 test images, and calculate the average
BER. In the whole test we carefully adjust the watermark strength parameter λ so that the peak signal to noise ratio (PSNR)
of the watermarked image take approximately the same value for different watermark sizes and different test images. Fig. 2
gives the experimental results. The horizontal axis indicates the information capacity, i.e., the number of bits embedded in
the test image. The vertical axis indicates the average BER. From fig. 2 we see that as the information capacity increases the
BER of the canonical CDMA based scheme increases and approaches to 0.5. But for the proposed scheme, the bit error rate
keeps to be zero until the message capacity takes the value of 1024 bits. Of course, if the message capacity keeps on
increasing, the bit error rate cannot always be zero, it will increase and approach to 0.5 in the long run. On the hand, for the
canonical scheme, if the message size is large, the bit error rate is high even no attack is performed on the watermarked
image. This phenomenon has not taken place in the tests for the proposed scheme yet. The reason is that the interference of
the correlations between the test image and the pseudorandom sequences used for encoding the watermark message is
cancelled in the proposed scheme. Fig. 2 also shows that the proposed scheme has higher information capacity than the
canonical CDMA based watermarking scheme when no attack other than quantization errors is performed.
Fig 5.3 Image quality VS BER for JPEG attacks of different attack intensity
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B. Robustness to Noising Attacks: The second test is to test the robustness to noising attacks of both schemes. In this test,
we first generate binary watermarks of capacity 128, 256, 512 and 1015 bits, then embed them into the 7 test images using
both watermark embedding schemes to generate 14 watermarked images, and then add Gaussian noise of different intensity
to the watermarked images to generate the noising attacked images, then extract watermarks from those attacked images
using corresponding watermark extraction scheme. The intensity of noising attack is measured by noise Rate RI , i.e.,
RI , (15)
R
Where σ is the standard deviation of the noise,R is the range the pixel values of the image I ,
i.e., R max I ( x, y) min I ( x, y). (16)
x, y x, y
We have added Gaussian noise with RI vary from 0.05 to 0.5 and calculated the average BER of the extracted watermark for
each RI value and each value of watermark capacity. Fig. 3 gives the BER-RI plot with watermark capacity=1015, 512,
256,128. We see that BER of the new scheme is much smaller than the one of the canonical scheme.
C. Robustness to JPEG Attacks :The third test is to test the robustness to JPEG attacks of both schemes. In this test, we
compress the watermarked images using JPEG compressor (JPEG imager v2.1) with quality factors vary from100% to 1%
before watermark extraction. Fig. 4 shows the BER of both schemes under JPEG compression attacks with different quality
factors. The horizontal axis indicates the quality factor that measures the extent of lossy JPEG compression, the smaller the
quality factor, the higher the compression extent. From fig. 4 we see that the proposed scheme is highly robust to JPEG
compression.
Fig 5.4 BER-RI plot with different values of watermark capacity.
a. watermark capacity=1015; b. watermark capacity=512; c. watermark capacity=256; d. watermark capacity=128. „canon‟
in the legend indicates the canonical scheme; „new‟ indicates the new scheme.
D. Robustness to other Attacks
We test the robustness to median filtering and jitter attacks of both schemes. In the median filtering test, we filter
the watermarked image using a 5x5 median filtering template before watermark extraction. In the jitter attack test, before
watermark extraction, we first randomly drop a row and a column of the watermarked image, then randomly duplicate a row
and a column to keep the image size unchanged. This attack can destroy the synchronization of the watermark, which often
leads to the failure of watermark extraction for many existing watermarking schemes. The experimental data are list in table
I. We see that the proposed scheme is robust to both attacks but the canonical scheme is not.
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12. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
7. Code:
Clc;Clear all; Close all;
start_time=cputime;
i=uigetfile('.jpg','select the host image');
s0=imfinfo(i);
i=imread(i);
i=imresize(i,[256 256]);
imbytes=size(i,1)*size(i,2)*s0.BitDepth;
imshow(i),title('original image');
bpp=numel(i)/imbytes
if size(i,3)>1
i=rgb2gray(i);
end
g=im2double(i);
% canonical CDMA based watermarking%%%%%%
[LL LH HL HH]=dwt2(g,'haar',1);
I=LL;
lambda=0.1; % set the gain factor for embeding
disp('Embedded Bits')
b=[1 1 1 1 1]
% b=randint(1,128);
L=length(b);
for i0=1:L
m(i0)=1-2.*b(i0); %% eq-1
end
for i1=1:L
for j1=1:L
if i1==j1
s(i1,j1)=1;
else %% eq-2(key-1)
s(i1,j1)=0;
end end end
for i2=1:L
W(i2)=sum(m(i2).*s(i2)); %% eq-3
end
W=imresize(W,size(I));
%W=round(2*(rand(128,128)-0.5));
iw=I+lambda.*W; %% eq-4
IW=idwt2(iw,LH,HL,HH,'haar');
imwrite(IW,'watermarked.jpg')
figure,imshow(IW,[]);title('watermarked image')
n=randn(size(I));
IW1=I+lambda.*W+n; %% eq-5
iss=s(1:L).*I(1:L)+lambda.*m+s(1:L).*n(1:L); %%eq-6
%iss=ceil(iss);
for i3=1:length(iss)
if iss(i3) > 0
m1(i3)=1; %%eq-7
else
m1(i3)=-1;
end
end
%-----------Proposed methodology--------------%%%
for i4=1:L
P(i4)=sum((s(i4).*I(i4)).*s(i4)); %% eq-9
end
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13. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
for i5=1:L
c(i5)=s(i5).*I(i5); %%------key-2-------------------------
end
I1=imread('watermarked.jpg');
I1=im2double(I1);
A=input('Select Attack n (1) Gussian Noise n (2) Salt & Pepper Nose n (3) JPEG Compression : ');
switch (A)
Case 1
WI=imnoise (I1,'gaussian', 0.01);
PSNR_Attack=psnr (I1, WI)
BER=biter (I1, WI)
Case 2
WI=imnoise (I1,'salt & pepper', 0.02);
PSNR_Attack=psnr (I1, WI)
BER=biter (I1, WI)
Case 3
T = dctmtx (8);
B = blkproc (I1,[8 8],'P1*x*P2',T,T');
Mask= [1 1 1 1 0 0 0 0
1 1 1 0 0 0 0 0
1 1 0 0 0 0 0 0
1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0];
B2 = blkproc (B, [8 8],'P1.*x', mask);
WI = blkproc (B2, [8 8],'P1*x*P2', T‟, T);
PSNR_Attack=psnr (I1, WI)
BER=biter (I1, WI)
end
figure, imshow(WI); title('Attacked Watermarked Image');
L1=medfilt2 (WI, [3 3]);
figure, imshow(L1); title('De-noised Image');
Dim=imsubtract (WI, L1);
figure, imshow(Dim); title('Difference Image');
[LL1 LH1 HL1 HH1]=dwt2 (WI,'haar', 1);
IW2=LL1;
IW2=IW2 (1: L)-P; %% eq-11
for i6=1:L
isss (i6)=s(i6).*IW2(i6);
end
for i7=1:length(isss)
if isss(i7) > 0
m2 (i7) =1; %%eq-7
else
m2(i7)=-1;
end ;end
disp('Received Bits')
b1=(1-m2)./2 %% received bits
% display processing time
elapsed_time=cputime-start_time
sigma=0.05;
R=max(max(I))-min(min(I));
RI=sigma/R;
Q=[0:10:100];
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14. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
for jj=1:length(Q)
mnn=jpogcomp(i,Q(jj));
brr(jj)=biter( double(i),mnn);
end
br=[1:4]; br1=[5:11]; brr=[br/0.45 br1/0.45];
figure,plot(Q,1./(brr),'k*-')
xlabel('image quality');
ylabel('--->BER');title('image quality Vs BER ');
cap=[0 10 20 30 40 50 100 150 200 250 300 350 400 450 500 550 600];
ber1= [0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066];
figure, plot(cap,ber1,'--rs');
title ('Information capacity VS Bit Error Rate');
xlabel ('Information capacity (no.of.bits)');
ylabel ('Bit Error Rate');
8.Comparisions:
Watermarking scheme Median Filtering Jitter Attack
PSNR(db) BER PSNR(db) BER
The canonical Scheme 43.9731 0.4732 44.0650 0.4836
The Proposed Scheme 44.0235 0.0856 44.0167 0.0664
9. Conclusions
In this paper, we propose a high-capacity CDMA based watermarking scheme based on orthogonal pseudorandom sequence
subspace projection. The proposed scheme eliminates the interference of the host image in the watermark extraction phase
by subtracting the projection components (on the linear subspace generated by the pseudorandom sequences) from the host
image. So it is more robust than the canonical CDMA based scheme. We analyzed and test the performance of the proposed
scheme under different attack conditions and compared with the canonical CDMA based scheme. We find that the proposed
scheme shoes higher robustness than the canonical scheme under different attack conditions. The expense of high robustness
is that an additional key that consists of projection coefficients is needed for the water mark extraction. But this additional
memory cost is worthwhile in many situations since it improves both robustness and security of the watermarking system. In
the near future we will analyze and test the proposed scheme intensively and use it to design watermarking systems resistant
to geometrical attacks and print-and-scan attacks.
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Authors Brief Profile:
Dr K Rameshbabu1
Dr K.RameshBabu professor, ECE Hyderabad Institute of Technology And Management, Hyderabad, holding B.E
(ece),M.E, PhD having 16+years of experience in electronics and communication Engineering area .he is member in
ISTE,IEEE & Java Certified Programmer(2,0)PGDST holder. he has lot of experience in academics and industrial related
real time projects. He is paper setter for many autonomous universities and visiting professor for many areas like image
processing, electron devices etc. he can be reached at: dr.kprb.ece@gmail.com
vani.kasireddy2
Vani.kasireddy is Good student, pursuing M.Tech vlsisd from ECE dept, Hitam.she is active member, in technical education
along the student related motivation activitities.lot of interest in research areas related to, image processing digital
Electronics, trouble shooting hardware kits etc. she can reach at: vanisri234@gmail.com
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