This document summarizes a proposed reversible watermarking technique based on bi-orthogonal wavelet transform. The technique embeds a watermark in the middle frequency sub-band (LH2) of the blue channel after decomposing the image using bi-orthogonal wavelet transform. The watermark is added to the wavelet coefficients using a pseudo-random sequence. The technique was evaluated using peak signal-to-noise ratio and normalized cross-correlation, and showed robustness against noise and geometric attacks. The technique aims to provide copyright protection while maintaining image quality and enabling original image recovery.
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-
This document presents a dual band video watermarking technique using 2D discrete wavelet transform (DWT) and 2-level singular value decomposition (SVD). A video is divided into frames and a watermark image is embedded into each frame. First, 2D DWT is applied to each frame, decomposing it into subbands. SVD is then applied to two subbands, converting them into matrices. The watermark image is embedded into the matrices. SVD is applied again and the matrices are multiplied with the original components for security. Experiments show the technique provides imperceptibility with a mean PSNR of 75.23dB and robustness against various attacks, with correlation values above 0.83.
Abstract: Watermarking is mainly projected for copy right protection, data safeguard, and data thrashing, etc. Nowadays all the communication requires protection. Estimation of video quality has a major role in today’s video distribution, communication control and e-commerce. Consumer fulfillment is achieved by providing good quality. Here the video input is changed into frames and the image set as watermark is embedded into the frames. The embedding process is carried out using DWT, then the embedded frame and other remaining frames are again changed into video file and it is transmitted. At the receiver side watermark image is extracted from the video. Finally, by using metrics such as TDR, PSNR the quality of watermark image is estimated under distortion. All experiments and tests are carried out using MATLAB.
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 Quick Glance over the Digital Watermarkingijsrd.com
Digital watermarking is a process for modifying physical or electronic media to embed a machine-readable code into the media. The media may be modified such that the embedded code is imperceptible or nearly imperceptible to the user, yet may be detected through an automated detection process. Watermarking is the art of imperceptibly embedding a message into a work. More than 700 years ago in Fabriano (Italy), paper watermarks appeared in handmade paper, in order to identify its provenance, format, and quality. In this context, the watermark is a kind of invisible signature that allows identifying the creator or the owner of a document, and to detect possible copyright violations, and especially non-authorized copying [1]. More recently, different watermarking techniques and strategies have been proposed in order to solve a number of problems, ranging from the detection of content manipulations, to information hiding (steganography), to document usage tracing. In particular, the insertion of multiple watermarks to trace a document during its lifecycle is a very interesting and challenging application [1]. The main property of the proposed method is that it allows the insertion of multiple watermarks by different users, who sequentially come into play one after the other and do not need any extra information besides the public keys. This characteristic makes the present approach more attractive than previously available solutions.
Implementation of digital image watermarking techniques using dwt and dwt svd...eSAT Journals
Abstract
These days, in every field there is gigantic utilization of computerized substance. Data took care of on web and mixed media system framework is in advanced structure. Computerized watermarking is only the innovation in which there is inserting of different data in advanced substance, which we need to shield from illicit replicating. Computerized picture watermarking is concealing data in any structure (content, picture, sound and video) in unique picture without corrupting its perceptual quality. On the off chance that of Discrete Wavelet Transform (DWT), deterioration of the first picture is completed to insert the watermark. Moreover, if there should arise an occurrence of cross breed system (DWT-SVD) firstly picture is decayed by and after that watermark is installed in solitary qualities acquired by application of Singular Value Decomposition (SVD). DWT and SVD are utilized in combination to enhance the nature of watermarking. We have the procedures which are looked at on the premise of Peak Signal to Noise Ratio (PSNR) esteem at various benefits of scaling component; high estimation of PSNR is coveted because it displays great intangibility of the strategy.
This document proposes a new digital watermarking technique that utilizes Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and Arnold Transform. It embeds a watermark image into the high frequency subbands of a cover image after applying DWT and SVD. During extraction, the receiver applies the same transforms to extract the watermark. The technique aims to provide improved robustness against various attacks like noise, compression, filtering etc. compared to existing techniques that use these transforms individually or in pairs. The document analyzes the proposed technique's performance based on the extracted watermark's peak signal-to-noise ratio after subjecting the watermarked image to 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-
This document presents a dual band video watermarking technique using 2D discrete wavelet transform (DWT) and 2-level singular value decomposition (SVD). A video is divided into frames and a watermark image is embedded into each frame. First, 2D DWT is applied to each frame, decomposing it into subbands. SVD is then applied to two subbands, converting them into matrices. The watermark image is embedded into the matrices. SVD is applied again and the matrices are multiplied with the original components for security. Experiments show the technique provides imperceptibility with a mean PSNR of 75.23dB and robustness against various attacks, with correlation values above 0.83.
Abstract: Watermarking is mainly projected for copy right protection, data safeguard, and data thrashing, etc. Nowadays all the communication requires protection. Estimation of video quality has a major role in today’s video distribution, communication control and e-commerce. Consumer fulfillment is achieved by providing good quality. Here the video input is changed into frames and the image set as watermark is embedded into the frames. The embedding process is carried out using DWT, then the embedded frame and other remaining frames are again changed into video file and it is transmitted. At the receiver side watermark image is extracted from the video. Finally, by using metrics such as TDR, PSNR the quality of watermark image is estimated under distortion. All experiments and tests are carried out using MATLAB.
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 Quick Glance over the Digital Watermarkingijsrd.com
Digital watermarking is a process for modifying physical or electronic media to embed a machine-readable code into the media. The media may be modified such that the embedded code is imperceptible or nearly imperceptible to the user, yet may be detected through an automated detection process. Watermarking is the art of imperceptibly embedding a message into a work. More than 700 years ago in Fabriano (Italy), paper watermarks appeared in handmade paper, in order to identify its provenance, format, and quality. In this context, the watermark is a kind of invisible signature that allows identifying the creator or the owner of a document, and to detect possible copyright violations, and especially non-authorized copying [1]. More recently, different watermarking techniques and strategies have been proposed in order to solve a number of problems, ranging from the detection of content manipulations, to information hiding (steganography), to document usage tracing. In particular, the insertion of multiple watermarks to trace a document during its lifecycle is a very interesting and challenging application [1]. The main property of the proposed method is that it allows the insertion of multiple watermarks by different users, who sequentially come into play one after the other and do not need any extra information besides the public keys. This characteristic makes the present approach more attractive than previously available solutions.
Implementation of digital image watermarking techniques using dwt and dwt svd...eSAT Journals
Abstract
These days, in every field there is gigantic utilization of computerized substance. Data took care of on web and mixed media system framework is in advanced structure. Computerized watermarking is only the innovation in which there is inserting of different data in advanced substance, which we need to shield from illicit replicating. Computerized picture watermarking is concealing data in any structure (content, picture, sound and video) in unique picture without corrupting its perceptual quality. On the off chance that of Discrete Wavelet Transform (DWT), deterioration of the first picture is completed to insert the watermark. Moreover, if there should arise an occurrence of cross breed system (DWT-SVD) firstly picture is decayed by and after that watermark is installed in solitary qualities acquired by application of Singular Value Decomposition (SVD). DWT and SVD are utilized in combination to enhance the nature of watermarking. We have the procedures which are looked at on the premise of Peak Signal to Noise Ratio (PSNR) esteem at various benefits of scaling component; high estimation of PSNR is coveted because it displays great intangibility of the strategy.
This document proposes a new digital watermarking technique that utilizes Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and Arnold Transform. It embeds a watermark image into the high frequency subbands of a cover image after applying DWT and SVD. During extraction, the receiver applies the same transforms to extract the watermark. The technique aims to provide improved robustness against various attacks like noise, compression, filtering etc. compared to existing techniques that use these transforms individually or in pairs. The document analyzes the proposed technique's performance based on the extracted watermark's peak signal-to-noise ratio after subjecting the watermarked image to different attacks.
SIGNIFICANCE OF RATIONAL 6TH ORDER DISTORTION MODEL IN THE FIELD OF MOBILE’S ...P singh
The document discusses a proposed method for video watermarking that uses spatial and frequency domain techniques for embedding watermark information, and tests the method's robustness against rational 6th order distortion. The key steps are: (1) extracting frames from a video and selecting the highest entropy frame, (2) using spread spectrum and LSB techniques to embed a watermark in the spatial domain and DWT in the frequency domain, (3) applying rational 6th order distortion to test the effect on the watermarked video, (4) calculating metrics like correlation, SSIM, PSNR, BER and MSE to evaluate the method and detect the watermark from the distorted video. The results show the values of correlation and SSIM
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.
IRJET-A study of video watermarking techniques based on energy modelIRJET Journal
Anchal Gupta,Rimanpal kaur,, "A study of video watermarking techniques based on energy model", International Research Journal of Engineering and Technology (IRJET), Vol2,issue-01 March 2015. p-ISSN:2395-0056, e-ISSN:2395-0072. www.irjet.net
Abstract
Recent years have witnessed rapid development in Digital video watermarking. Security and copyright protection are getting to be imperative issues in media applications and administrations. Video watermarking is relatively a new technology that has been proposed to solve the problem of illegal manipulation and distribution of digital video. It is the process of embedding copyright information in video bit streams. Most of the proposed video watermarking schemes are based on the techniques of image watermarking. But video watermarking introduces some issues not present in image watermarking. In this paper, we perform a survey on available video watermarking techniques and it provides a critical review on various available techniques.
Commutative approach for securing digital mediaijctet
This document summarizes a paper on digital image watermarking techniques. It discusses how digital watermarking can be used to embed hidden information in multimedia data like images, audio, and video to identify ownership and protect against illegal copying. It describes different watermarking techniques including the discrete cosine transform (DCT) and discrete wavelet transform (DWT). The paper analyzes the DCT and DWT techniques, evaluating them using peak signal-to-noise ratio at different threshold values. It finds that the DWT technique provides better image quality than DCT. The document also discusses applications of digital watermarking like ownership assertion, fingerprinting, copy prevention and control, fraud detection, and ID card security.
A Hybrid Model of Watermarking Scheme for Color Image Authentication Using Di...iosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed 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.
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.
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.
The document discusses digital image watermarking techniques for copyright protection. It introduces watermarking requirements like perceptual transparency and robustness. Common watermarking techniques include spatial-domain methods that modify pixel values and transform-domain methods that modify frequency coefficients. Transform techniques can provide stronger, more robust watermarks but are also more computationally expensive. The document also outlines various attacks against watermarked images and applications of digital watermarking technology.
A novel attack detection technique to find attack in watermarked images with ...prjpublications
The document describes a novel technique for detecting attacks on watermarked images. The technique uses Peak Signal-to-Noise Ratio (PSNR) and RGB color intensity values to analyze differences between an original watermarked image and a tested watermarked image. If the PSNR value is above a threshold, the images are considered identical. Otherwise, RGB intensity levels are compared. A match suggests no attack, while a mismatch identifies an attacked image and the type of attack applied. The technique is demonstrated by hiding data in an image, applying attacks, and correctly detecting the attacks based on PSNR and RGB analyses. Evaluation shows the approach effectively identifies various watermark attacks.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Watermarking Scheme based on Redundant Discrete Wavelet Transform and SVDIRJET Journal
This document presents a watermarking scheme based on Redundant Discrete Wavelet Transform (RDWT) and Singular Value Decomposition (SVD) with the following key points:
1. The host image is first transformed into the wavelet domain using 1-level RDWT. SVD is then applied to embed the watermark by modifying the singular values of the host and watermark images.
2. For extraction, the watermarked image is transformed using RDWT and SVD to recover the singular values. The extracted watermark is obtained by calculating the difference between the singular values of the watermarked and original host images.
3. Experimental results on standard test images show the scheme is robust against various attacks
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.
A NOVEL APPROACH FOR IMAGE WATERMARKING USING DCT AND JND TECHNIQUESijiert bestjournal
Today�s world is digital world. Nowadays,in every field there is enormous use of digital contents. Information handled on internet and multimedia netw ork system is in digital form. The copying of digital content without quality loss is not so diff icult. Due to this,there are more chances of copyi ng of such digital information. So,there is great need o f prohibiting such illegal copyright of digital med ia. Digital watermarking is the powerful solution to ad dress this problem. Digital watermarking is the technology in which there is embedding of various t ypes of information in digital content which we have to protect from illegal copying. This embedded information to protect the data is embedded as watermark. This paper introduces two novel techniqu es for image watermarking such as DCT and JND. The DCT based approach adapted to embed waterm arks in DC,low,mid and high frequency components coefficient of DCT. The JND based approa ch gives robust and transparent scheme of watermarking that exploits the �human visual system s� sensitivity to local image characteristics obtained from the spatial domain,improving upon th e content based image watermarking scheme.
IRJET-Comparative Analysis of DCT and DWT based novel methods for WatermarkingIRJET Journal
This document presents a comparative analysis of DCT and DWT based watermarking techniques. It proposes algorithms for embedding and extracting watermarks in the DCT and DWT domains. The DWT method embeds the watermark in the low-low frequency region and is shown to be more robust to attacks like noise addition and region tampering compared to the DCT method. Results demonstrate that DWT provides better imperceptibility and fidelity than DCT, especially at lower embedding strengths. However, robustness decreases with lower strengths. An embedding strength of 10 is identified as a good balance for all parameters. In conclusion, DWT is found to be a better technique than DCT for digital image watermarking applications.
IRJET-Reversible Image Watermarking Based on Histogram Shifting TechniqueIRJET Journal
This document summarizes a research paper on reversible image watermarking based on histogram shifting technique. It begins with an abstract that describes reversible watermarking and its goal of allowing exact recovery of the original image. It then provides background on reversible watermarking categories and discusses histogram shifting methods. The document outlines the proposed system, which aims to minimize distortion by selecting an optimal embedding point. It presents results comparing the proposed technique to previous histogram shifting methods, showing improved PSNR. Finally, it concludes the proposed method reduces image distortion through optimal embedding point selection.
here it introduces an efficient multi-resolution watermarking methodology for copyright protection of digital images. By adapting the watermark signal to the wavelet coefficients, the proposed method is highly image adaptive and the watermark signal can be strengthen in the most significant parts of the image. As this property also increases the watermark visibility, usage of the human visual system is incorporated to prevent perceptual visibility of embedded watermark signal. Experimental results show that the proposed system preserves the image quality and is vulnerable against most common image processing distortions. Furthermore, the hierarchical nature of wavelet transform allows for detection of watermark at various resolutions, resulting in reduction of the computational load needed for watermark detection based on the noise level. The performance of the proposed system is shown to be superior to that of other available schemes reported in the literature.
This document presents an algorithm for imperceptibly embedding a DNA-encoded watermark into a color image for authentication purposes. It applies a multi-resolution discrete wavelet transform to decompose the image. The watermark, encoded into DNA nucleotides, is then embedded into the third-level wavelet coefficients through a quantization process. Specifically, the watermark nucleotides are complemented and used to quantize coefficients in the middle frequency band, modifying the coefficients. The watermarked image is reconstructed through inverse wavelet transform. Extraction reverses these steps to recover the watermark without the original image. The algorithm aims to balance imperceptibility and robustness through this wavelet-based, blind watermarking scheme.
1) The document proposes a hybrid digital watermarking scheme that uses both discrete wavelet transform (DWT) and singular value decomposition (SVD) for color image authentication.
2) In the proposed scheme, the watermark is embedded in the singular values of the DWT sub-bands of the cover image, rather than directly on the wavelet coefficients. This reduces computational expense compared to other DWT-SVD methods.
3) Experimental results on test images show that the hybrid DWT-SVD scheme provides better imperceptibility and robustness against various attacks compared to using DWT or SVD alone. The recovered watermarks had high quality even after the watermarked images were distorted.
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.
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
BLIND EXTRACTION OF DIGITAL WATERMARKING ALGORITHM FOR COLOR IMAGESijma
Digital watermark technology hides copyright information in digital images, effectively protecting the
copyright of digital images. At present, the color image digital watermarking algorithm still has defects
such as the inability to balance robustness, invisibility and the weak anti-attack ability. Aiming at the
above problems, this paper studies the digital watermarking method based on discrete wavelet transform
and discrete cosine transform. Then this paper proposes a color image blind digital watermarking
algorithm based on QR code. First, convert the color image from RGB space to YCbCr space, extract the Y
component and perform the second-level discrete wavelet transform. secondly, block the LL2 subband and
perform the discrete cosine transform. finally, use the embedding method to convert the watermark
information after the Arnold transform embedded in the block. The experimental results show that the
PSNR of the color image embedded with the QR code is 56.7159 without being attacked. After being
attacked, its PSNR and NC values are respectively 30dB and 0.95 or more, which proves that the algorithm
has good robustness and can achieve watermarking blind extraction.
BLIND EXTRACTION OF DIGITAL WATERMARKING ALGORITHM FOR COLOR IMAGESijma
Digital watermark technology hides copyright information in digital images, effectively protecting the
copyright of digital images. At present, the color image digital watermarking algorithm still has defects
such as the inability to balance robustness, invisibility and the weak anti-attack ability. Aiming at the
above problems, this paper studies the digital watermarking method based on discrete wavelet transform
and discrete cosine transform. Then this paper proposes a color image blind digital watermarking
algorithm based on QR code. First, convert the color image from RGB space to YCbCr space, extract the Y
component and perform the second-level discrete wavelet transform. secondly, block the LL2 subband and
perform the discrete cosine transform. finally, use the embedding method to convert the watermark
information after the Arnold transform embedded in the block. The experimental results show that the
PSNR of the color image embedded with the QR code is 56.7159 without being attacked. After being
attacked, its PSNR and NC values are respectively 30dB and 0.95 or more, which proves that the algorithm
has good robustness and can achieve watermarking blind extraction.
SIGNIFICANCE OF RATIONAL 6TH ORDER DISTORTION MODEL IN THE FIELD OF MOBILE’S ...P singh
The document discusses a proposed method for video watermarking that uses spatial and frequency domain techniques for embedding watermark information, and tests the method's robustness against rational 6th order distortion. The key steps are: (1) extracting frames from a video and selecting the highest entropy frame, (2) using spread spectrum and LSB techniques to embed a watermark in the spatial domain and DWT in the frequency domain, (3) applying rational 6th order distortion to test the effect on the watermarked video, (4) calculating metrics like correlation, SSIM, PSNR, BER and MSE to evaluate the method and detect the watermark from the distorted video. The results show the values of correlation and SSIM
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.
IRJET-A study of video watermarking techniques based on energy modelIRJET Journal
Anchal Gupta,Rimanpal kaur,, "A study of video watermarking techniques based on energy model", International Research Journal of Engineering and Technology (IRJET), Vol2,issue-01 March 2015. p-ISSN:2395-0056, e-ISSN:2395-0072. www.irjet.net
Abstract
Recent years have witnessed rapid development in Digital video watermarking. Security and copyright protection are getting to be imperative issues in media applications and administrations. Video watermarking is relatively a new technology that has been proposed to solve the problem of illegal manipulation and distribution of digital video. It is the process of embedding copyright information in video bit streams. Most of the proposed video watermarking schemes are based on the techniques of image watermarking. But video watermarking introduces some issues not present in image watermarking. In this paper, we perform a survey on available video watermarking techniques and it provides a critical review on various available techniques.
Commutative approach for securing digital mediaijctet
This document summarizes a paper on digital image watermarking techniques. It discusses how digital watermarking can be used to embed hidden information in multimedia data like images, audio, and video to identify ownership and protect against illegal copying. It describes different watermarking techniques including the discrete cosine transform (DCT) and discrete wavelet transform (DWT). The paper analyzes the DCT and DWT techniques, evaluating them using peak signal-to-noise ratio at different threshold values. It finds that the DWT technique provides better image quality than DCT. The document also discusses applications of digital watermarking like ownership assertion, fingerprinting, copy prevention and control, fraud detection, and ID card security.
A Hybrid Model of Watermarking Scheme for Color Image Authentication Using Di...iosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed 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.
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.
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.
The document discusses digital image watermarking techniques for copyright protection. It introduces watermarking requirements like perceptual transparency and robustness. Common watermarking techniques include spatial-domain methods that modify pixel values and transform-domain methods that modify frequency coefficients. Transform techniques can provide stronger, more robust watermarks but are also more computationally expensive. The document also outlines various attacks against watermarked images and applications of digital watermarking technology.
A novel attack detection technique to find attack in watermarked images with ...prjpublications
The document describes a novel technique for detecting attacks on watermarked images. The technique uses Peak Signal-to-Noise Ratio (PSNR) and RGB color intensity values to analyze differences between an original watermarked image and a tested watermarked image. If the PSNR value is above a threshold, the images are considered identical. Otherwise, RGB intensity levels are compared. A match suggests no attack, while a mismatch identifies an attacked image and the type of attack applied. The technique is demonstrated by hiding data in an image, applying attacks, and correctly detecting the attacks based on PSNR and RGB analyses. Evaluation shows the approach effectively identifies various watermark attacks.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Watermarking Scheme based on Redundant Discrete Wavelet Transform and SVDIRJET Journal
This document presents a watermarking scheme based on Redundant Discrete Wavelet Transform (RDWT) and Singular Value Decomposition (SVD) with the following key points:
1. The host image is first transformed into the wavelet domain using 1-level RDWT. SVD is then applied to embed the watermark by modifying the singular values of the host and watermark images.
2. For extraction, the watermarked image is transformed using RDWT and SVD to recover the singular values. The extracted watermark is obtained by calculating the difference between the singular values of the watermarked and original host images.
3. Experimental results on standard test images show the scheme is robust against various attacks
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.
A NOVEL APPROACH FOR IMAGE WATERMARKING USING DCT AND JND TECHNIQUESijiert bestjournal
Today�s world is digital world. Nowadays,in every field there is enormous use of digital contents. Information handled on internet and multimedia netw ork system is in digital form. The copying of digital content without quality loss is not so diff icult. Due to this,there are more chances of copyi ng of such digital information. So,there is great need o f prohibiting such illegal copyright of digital med ia. Digital watermarking is the powerful solution to ad dress this problem. Digital watermarking is the technology in which there is embedding of various t ypes of information in digital content which we have to protect from illegal copying. This embedded information to protect the data is embedded as watermark. This paper introduces two novel techniqu es for image watermarking such as DCT and JND. The DCT based approach adapted to embed waterm arks in DC,low,mid and high frequency components coefficient of DCT. The JND based approa ch gives robust and transparent scheme of watermarking that exploits the �human visual system s� sensitivity to local image characteristics obtained from the spatial domain,improving upon th e content based image watermarking scheme.
IRJET-Comparative Analysis of DCT and DWT based novel methods for WatermarkingIRJET Journal
This document presents a comparative analysis of DCT and DWT based watermarking techniques. It proposes algorithms for embedding and extracting watermarks in the DCT and DWT domains. The DWT method embeds the watermark in the low-low frequency region and is shown to be more robust to attacks like noise addition and region tampering compared to the DCT method. Results demonstrate that DWT provides better imperceptibility and fidelity than DCT, especially at lower embedding strengths. However, robustness decreases with lower strengths. An embedding strength of 10 is identified as a good balance for all parameters. In conclusion, DWT is found to be a better technique than DCT for digital image watermarking applications.
IRJET-Reversible Image Watermarking Based on Histogram Shifting TechniqueIRJET Journal
This document summarizes a research paper on reversible image watermarking based on histogram shifting technique. It begins with an abstract that describes reversible watermarking and its goal of allowing exact recovery of the original image. It then provides background on reversible watermarking categories and discusses histogram shifting methods. The document outlines the proposed system, which aims to minimize distortion by selecting an optimal embedding point. It presents results comparing the proposed technique to previous histogram shifting methods, showing improved PSNR. Finally, it concludes the proposed method reduces image distortion through optimal embedding point selection.
here it introduces an efficient multi-resolution watermarking methodology for copyright protection of digital images. By adapting the watermark signal to the wavelet coefficients, the proposed method is highly image adaptive and the watermark signal can be strengthen in the most significant parts of the image. As this property also increases the watermark visibility, usage of the human visual system is incorporated to prevent perceptual visibility of embedded watermark signal. Experimental results show that the proposed system preserves the image quality and is vulnerable against most common image processing distortions. Furthermore, the hierarchical nature of wavelet transform allows for detection of watermark at various resolutions, resulting in reduction of the computational load needed for watermark detection based on the noise level. The performance of the proposed system is shown to be superior to that of other available schemes reported in the literature.
This document presents an algorithm for imperceptibly embedding a DNA-encoded watermark into a color image for authentication purposes. It applies a multi-resolution discrete wavelet transform to decompose the image. The watermark, encoded into DNA nucleotides, is then embedded into the third-level wavelet coefficients through a quantization process. Specifically, the watermark nucleotides are complemented and used to quantize coefficients in the middle frequency band, modifying the coefficients. The watermarked image is reconstructed through inverse wavelet transform. Extraction reverses these steps to recover the watermark without the original image. The algorithm aims to balance imperceptibility and robustness through this wavelet-based, blind watermarking scheme.
1) The document proposes a hybrid digital watermarking scheme that uses both discrete wavelet transform (DWT) and singular value decomposition (SVD) for color image authentication.
2) In the proposed scheme, the watermark is embedded in the singular values of the DWT sub-bands of the cover image, rather than directly on the wavelet coefficients. This reduces computational expense compared to other DWT-SVD methods.
3) Experimental results on test images show that the hybrid DWT-SVD scheme provides better imperceptibility and robustness against various attacks compared to using DWT or SVD alone. The recovered watermarks had high quality even after the watermarked images were distorted.
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.
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
BLIND EXTRACTION OF DIGITAL WATERMARKING ALGORITHM FOR COLOR IMAGESijma
Digital watermark technology hides copyright information in digital images, effectively protecting the
copyright of digital images. At present, the color image digital watermarking algorithm still has defects
such as the inability to balance robustness, invisibility and the weak anti-attack ability. Aiming at the
above problems, this paper studies the digital watermarking method based on discrete wavelet transform
and discrete cosine transform. Then this paper proposes a color image blind digital watermarking
algorithm based on QR code. First, convert the color image from RGB space to YCbCr space, extract the Y
component and perform the second-level discrete wavelet transform. secondly, block the LL2 subband and
perform the discrete cosine transform. finally, use the embedding method to convert the watermark
information after the Arnold transform embedded in the block. The experimental results show that the
PSNR of the color image embedded with the QR code is 56.7159 without being attacked. After being
attacked, its PSNR and NC values are respectively 30dB and 0.95 or more, which proves that the algorithm
has good robustness and can achieve watermarking blind extraction.
BLIND EXTRACTION OF DIGITAL WATERMARKING ALGORITHM FOR COLOR IMAGESijma
Digital watermark technology hides copyright information in digital images, effectively protecting the
copyright of digital images. At present, the color image digital watermarking algorithm still has defects
such as the inability to balance robustness, invisibility and the weak anti-attack ability. Aiming at the
above problems, this paper studies the digital watermarking method based on discrete wavelet transform
and discrete cosine transform. Then this paper proposes a color image blind digital watermarking
algorithm based on QR code. First, convert the color image from RGB space to YCbCr space, extract the Y
component and perform the second-level discrete wavelet transform. secondly, block the LL2 subband and
perform the discrete cosine transform. finally, use the embedding method to convert the watermark
information after the Arnold transform embedded in the block. The experimental results show that the
PSNR of the color image embedded with the QR code is 56.7159 without being attacked. After being
attacked, its PSNR and NC values are respectively 30dB and 0.95 or more, which proves that the algorithm
has good robustness and can achieve watermarking blind extraction.
This document proposes a new digital watermarking technique that uses a combination of Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and Arnold Transform. It embeds a watermark into the high frequency DWT subbands of an image by modifying the SVD singular values. During extraction, the receiver applies DWT, SVD, and Arnold Transform in reverse order to recover the watermark. The technique aims to provide improved robustness against various attacks like noise, compression, and image processing operations. The document evaluates the proposed technique on standard test images and finds it achieves good imperceptibility and resistance to cropping, rotation, noise, filtering, compression, and
A Brief Survey on Robust Video Watermarking Techniquestheijes
This document provides a survey of robust video watermarking techniques. It begins with an abstract discussing digital watermarking and its role in copyright protection as the growth of multimedia on the internet has led to more copyright issues. The document then reviews various video watermarking methods and factors like robustness, security, and perceptual fidelity. It discusses approaches like spatial domain and transform domain watermarking techniques that use discrete cosine transform, fast Fourier transform, and discrete wavelet transform. The document also provides a table comparing different video watermarking methods from past literature and concludes that watermarking combined with other cryptographic techniques can provide effective copyright protection for video.
DIGITAL IMAGE WATERMARKING USING DFT ALGORITHMacijjournal
The document discusses digital image watermarking using the discrete Fourier transform (DFT) algorithm. It reviews existing watermarking techniques and metrics for evaluating watermarking schemes. It then proposes using DFT for watermarking. The key steps of the DFT watermarking method are described, including embedding the watermark in the frequency domain. Test results show the PSNR and MSE values for sample watermarked images. In conclusion, the DFT approach successfully completes watermark embedding and extraction.
PERCEPTUAL COPYRIGHT PROTECTION USING MULTIRESOLUTION WAVELET-BASED WATERMARK...gerogepatton
In this paper, an efficiently DWT-based watermarking technique is proposed to embed signatures in images to attest the owner identification and discourage the unauthorized copying. This paper deals with a fuzzy inference filter to choose the larger entropy of coefficients to embed watermarks. Unlike most previous watermarking frameworks which embedded watermarks in the larger coefficients of inner coarser subbands, the proposed technique is based on utilizing a context model and fuzzy inference filter by embedding watermarks in the larger-entropy coefficients of coarser DWT subbands. The proposed approaches allow us to embed adaptive casting degree of watermarks for transparency and robustness to the general image-processing attacks such as smoothing, sharpening, and JPEG compression. The approach has no need the original host image to extract watermarks. Our schemes have been shown to provide very good results in both image transparency and robustness.
ROBUST IMAGE WATERMARKING METHOD USING WAVELET TRANSFORMsipij
In this paper a robust watermarking method operating in the wavelet domain for grayscale digital imagesis developed. The method first acomputes the differences between the watermark and the HH1 sub-band ofthe cover image values and then embed these differences in one of the frequency sub-bands. The resultsshow that embedding the watermark in the LH1 sub-band gave the best results. The results were evaluatedusing the RMSE and the PSNR of both the original and the watermarked image. Although the watermarkwas recovered perfectly in the ideal case, the addition of Gaussian noise, or compression of the imageusing JPEG with quality less than 100 destroys the embedded watermark. Different experiments werecarried out to test the performance of the proposed method and good results were obtained.
Report on Digital Watermarking Technology vijay rastogi
Digital watermarking is the process of embedding information into digital multimedia content such that the information (which we call the watermark) can later be extracted or detected for a variety of purposes including copy prevention and control.
This document summarizes a research paper on lossless reversible visible watermarking. It begins with an abstract that describes digital watermarking techniques and introduces a new approach for lossless reversible visible watermarking with robust security. It then provides a literature review of previous lossless invisible and visible watermarking techniques. The proposed technique aims to allow legitimate users to remove embedded watermarks and perfectly recover the original image content. Finally, it outlines the system architecture and provides a mathematical model for the watermark generation, embedding, and extraction processes.
This document summarizes a research paper on lossless reversible visible watermarking. It begins with an abstract that describes digital watermarking techniques and introduces a new approach for lossless reversible visible watermarking with robust security. It then provides a literature review of previous lossless invisible and visible watermarking techniques. The proposed technique aims to allow legitimate users to remove embedded watermarks and perfectly recover the original image content. Finally, it outlines the system architecture and provides a mathematical model for the watermark generation, embedding, and extraction processes.
Digital Image Watermarking using Discrete Wavelet TransformIRJET Journal
This document discusses using discrete wavelet transform (DWT) for digital image watermarking. It begins with an abstract describing how digital watermarking can protect copyright by embedding verification messages into digital images. The paper then provides more details on the general process of watermark embedding and extraction. For the watermarking scheme proposed, DWT is used to decompose images into frequency subbands before the watermark is embedded into the cover image's low frequency approximation subband using alpha blending. Experimental results demonstrate embedding a watermark image into a cover image and then successfully extracting the watermark. The DWT technique provides robust watermarking against common image processing operations.
Reversible Image Watermarking Based on Histogram Shifting TechniqueIRJET Journal
This document discusses a reversible image watermarking technique based on histogram shifting. Reversible watermarking allows for the exact recovery of the original image after extracting the embedded watermark. The histogram shifting technique embeds data by shifting pixel values between the maximum and minimum points in the image histogram. This achieves watermarking with low computational complexity and distortion. The document proposes an improved method that selects an optimal pixel value as the "embedding point" to minimize the number of pixels shifted, and thus reduce distortion compared to existing histogram shifting algorithms. Results demonstrating the reversible watermarking process on sample images are presented.
A review-on-digital-image-watermarking-techniquesEditor IJMTER
Due to the rapid expansion in internet technology copyright protection and data
authenticity are two major problems in handling digital multimedia. Watermarking is a very
important field for copyrights of various electronic documents and media. A variety of
techniques have been proposed for copyright protection of digital images which include
spatial domain and transform domain watermarking. This paper aims to provide some basic
concepts of digital image watermarking techniques and comparisons between them.
This document summarizes a research paper on a relational database watermarking technique using clustering. The proposed technique clusters database tuples before embedding and detecting a watermark. It uses Mahalanobis distance to measure tuple similarity during clustering. The watermark is then embedded and detected within each cluster by modifying the least significant bits of numeric fields. Majority decision is used in blind detection to determine watermark bits. The technique aims to improve watermark robustness against database operations while maintaining reversibility.
This document discusses an enhanced technique for secure and reliable watermarking using Modified Haar Wavelet Transform (MFHWT). The proposed technique embeds a watermark into an original image using discrete wavelet transform (DWT) and wavelet packet transform (WPT) according to the size of the watermark. MFHWT is a memory efficient, fast, and simple transform. The watermarking process involves embedding and extraction processes. Various watermarking techniques in different transform domains are discussed, including DWT and WPT. The proposed algorithm uses MFHWT for decomposition and reconstruction. Image quality is measured using metrics like MSE and PSNR, with higher PSNR indicating better quality. The technique achieves robustness
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Nordic Marketo Engage User Group_June 13_ 2024.pptx
M0262076085
1. International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org Volume 2 Issue 6 ǁ June. 2013 ǁ PP.76-85
www.ijesi.org 76 | Page
Reversible watermarking Based on Bi-orthogonal wavelet
Transform
Dr.Arvind.H.S, Neha Shankar
ECE Dept, RITM, Bangalore, Karnataka, India
ABSTRACT: As the popularity of Digital Medias is growing faster the intellectual property needs copyright
protection. For the prevention of illegal verification and copying of content integrity. Therefore new data hiding
techniques has to be developed that satisfy the requirements of Robustness ,Imperceptibility, Capacity or data
hiding rate and the security of the hidden data. Watermarking has been utilized widely by researchers to
provide security to the digital documents. In this project I have proposed a method which is an efficient
technique for protecting the copyrights of digital images with the aid of “Watermarking”. I have implemented
watermarking algorithm in the frequency domain which is based on the Bi-orthogonal Wavelet Transform.
Watermark is embedded by modifying the coefficients of the middle frequency sub band within region of non
interest by which the visibility of the image and diagnosis capability will not be affected. Therefore the attacks
on the image will not be able to remove the watermark.I have selected the blue channel of the cover image for
embedding the watermark because it is more resistant to changes when compared to red and green channels
Blue channel is decomposed into n-level by using bi-orthogonal wavelet transform because bi-orthogonal
wavelet transform is an invertible transform and it has the property of exact reconstruction and smoothness. The
horizontal and vertical sub bands are selected for the embedding watermark. This proposed method is shown to
be robust against many geometric attacks and signal processing operations.
INDEXTERMS: Digital Image Watermarking, Biorthogonal Wavelets Transform, Robust Watermarking,
Copyright Protection, Attacks.
I. INTRODUCTION
In 13th
century watermarks were used to paper brand and the mill that produced it in Italy and in 18th
century watermarks were used as the anti-counterfeiting measures on money and other documents. In 1995 the
field of watermarking started to bloom, from past five years intense research has been carried out in this field,
which has led to the discovery of various algorithms. Currently there are many techniques for embedding digital
watermarks. The desired information is directly written onto images or audio data digitally, in such a manner
that the audio data or images are not damaged. In the process of embedding a watermark should not result in a
significant reduction or increase in the original data.
1.1 Principle of watermarking:
Fig 1.1: Watermarking block diagram
A watermarking system is divided into two distinct steps. They are embedding and detection. In embedding
process the proposed algorithm accepts the host and the data to be embedded, and a watermarked signal is
produced. The watermarked signal is then transmitted or stored. The obtained watermarked image is passed
through a decoder in which a reverse algorithm is applied to retrieve the watermark. The different techniques
uses different ways of embedding watermark onto the cover object. During embedding and extraction process a
secret key to prevent illegal access to watermark. For a practical and useful watermarking scheme it has to meet
the following requirements:Robustness: Robustness means a digital watermarking scheme should be able to
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resist the watermark attacks or modifications like resizing, file compression, rotation etc made to the original
file. On the other hand, several intentional or unintentional attacks may be incurred to remove the embedded
watermark. Thus, the watermarked image has to survive the legitimate usage such as resamples, conversions,
lossy compressions and other malicious operations. A robust watermarking scheme should recognize the
retrieved watermark and the image quality should not be seriously harmed. Imperceptibility:A visible or
invisible watermark can be embedded into an image, the visible watermark is perceptible and it is just like noise.
Using a noise removal process we can remove the visible watermark. In order to reduce this risk of cracking,
most of the proposed watermarking techniques use invisible watermarks. On the other hand, the quality of the
watermarked image is also very important. If in the process of embedding watermark, the quality of the
watermarked image is affected, then the watermarked image will lose its value or even draw the attention of the
attackers. Imperceptibility is a very important requirement therefore the quality between the original image and
the watermarked image should not be seriously degraded.Readily embedding and retrieving: The watermark
should be securely and easily embedded and retrieved by the owner of the original image.Data load or capacity:
Data load or capacity means the maximum amount of data that can be embedded into the image to ensure proper
retrieval of the watermark during extraction. Blind: Some of the conventional watermarking schemes require the
help of the original image in order to retrieve the embedded watermark. But the reversible watermarking
schemes has the ability to recover the original image from the watermarked image directly. As the retrieval
process doesn‟t need the original image, we reversible watermarking as blind.Transparency: This refers to the
perceptual similarity between the watermarked image and the original image. The inserted watermark should be
imperceptible. The watermark may lead to the degradation in the quality of the digital content, but in some
applications a small amount of degradation may be accepted to get higher robustness.
Fig 1.2: Conventional and reversible watermarking schemes
In the figure 1.2 the procedure of conventional and reversible watermarking schemes is illustrated. The
steps of conventional watermarking and reversible watermarking are similar but there is an additional function
to recover the original image from the suspected image. Therefore, the reversible watermarking is very much
suitable for the applications that require high quality images such as medical and military images.
II. METHODOLOGY
Wavelets are the mathematical functions that differentiate data into different frequency components,
and each component is studied with the resolution matched to its scale. They are more advantageous compared
to Fourier methods in analyzing physical situations, in which the signal contains sharp spikes and
discontinuities. Wavelets were developed independently in the fields of quantum physics, mathematics, seismic
geology and electrical engineering. Image compression, image denoising, watermarking, human vision,
turbulence earthquake prediction and radar are the applications of wavelet that have emerged during last ten
years by the interchanges between the above mentioned fields. A wave is an oscillating function of space or time
that is periodic. It is an infinite length continuous function in space or time. In contrast, wavelets are localized
waves. A wavelet is a waveform of a limited duration that has an average value of zero. A function can be called
a wavelet if it poses some of the properties such as, the function is either oscillatory or has a wavy appearance, it
should have good space localization or in other words it should be confined to a finite interval and it should
have sufficient decay in frequency.
Wavelet transform has achieved more attention in the field of image processing due to its ability in
adapting to visual characteristics and its flexibility in representing non-stationary image signals. Wavelet
transforms are most widely used and the most powerful tool in the field of image processing. A wavelet
transform divides a signal into many segments corresponding to different frequency bands.Fourier transform is a
powerful tool that has been used by the signal analysts for many years. It gives information regarding frequency
component of the signal. The main problem of using Fourier transform is that frequency analysis cannot offer
both time resolution and good frequency at the same time. A Fourier transform will not give any information
about the time at which the particular frequency has occurred in the signal. Hence Fourier transform is not that
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effective tool to analyze the non-stationary signal. Therefore, to overcome this problem, short time Fourier
transforms or windowed Fourier transform was introduced. Even though a windowed Fourier transform has the
ability to give time information, multi resolution is not possible. So wavelets were introduced as the answer for
this problem. A wavelet has a unique property of not having a fixed width sampling window. The wavelet
transform can be classified into discrete wavelet transform and continuous wavelet transform. As the continuous
wavelet transforms needs to integrate over all times of long signals, it can be bit time consuming. To overcome
this problem discrete wavelet transform was introduced. Using sub band coding discrete wavelet transform can
be implemented. The discrete wavelet transform is very much useful in image processing because it can
simultaneously localize signals in scale and time. But Discrete Fourier transform and discrete cosine transform
can localize signals only in frequency domain. The discrete wavelet transform can be obtained by performing
filtering operation on the signal by using a series of digital filters at different scales. The scaling operating can
be performed by changing the resolution of the signal by using the process of sub sampling. The discrete
wavelet transform can be computed using convolution based or lifting based procedures. The input sequences
are decomposed into high pass and low pass sub bands in both the methods. Each consist of the half the number
of samples in the original sequence.
2.1 2D wavelet transform:
A 2D wavelet transform is equivalent to two 1D wavelet transform in series. A 2D discrete wavelet
transform is computed by using high pass and low pass filtering of the image pixels. In the figure 3.1 shown
below, the low pass filters are denoted by H(z) and the high pass filters are denoted by G(n). This figure clearly
depicts the two level of 3D discrete wavelet transform decomposition. At each level, the low pass filter
generates the coarse approximation of the input image and the high pass filter produces the detailed pixel
information of the image.The DWT of a signal „X‟ is calculated by using a mathematical equation 1 given
below. First the samples will be passed through a low pass filter which has an impulse response „g‟ and it is also
decomposed simultaneously using a high pass filter denoted by „h‟. The filter outputs are sub sampled by 2.
Fig 2.1: One level filter bank for the computation of 2D DWT
At the end of each high pass and low pass filter, the outputs are down sampled by 2. For
computing 2D discrete wavelet transform, 1D discrete wavelet transform should be applied twice in both
vertical and horizontal dimension. Or we can also say that a 2D discrete wavelet transform can be performed by
first performing 1D DWT on each row of the image followed by 1D DWT on each column. Performing 1D
DWT on row is called as horizontal filtering and on columns is called as vertical filtering.
Fig 2.2: one level filter bank for computation of 2D IDWT
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The figure 2.2 shows the one level filter banks for the inverse discrete wavelet transform. In the
discrete wavelet transform, the taken image signal can be analyzed by passing the image through an analysis
filter bank which is followed by decimation operation. When the signal is passed through the filters, it is split
into two bands. A two dimensional DWT is accomplished by performing one dimensional transform two times.
And by using synthesis filter the image is reconstructed back.
Fig 2.3: Two level 2D decomposition
The above figure 2.3 shows the structure of two level 2D discrete wavelet transform decomposition. For
multiple levels of decomposition LL1 band will be iteratively decomposed. LL band contains approximation co-
efficients. HL sub band contains horizontal details. LH sub band consists of vertical details and HH sub band
consists of diagonal details. LL sub band not only contains coarse approximation of the image but it also
contains most of the image‟s energy co-efficients.Usually wavelets can be either orthogonal (orthonormal) or bi-
orthogonal. Earlier most of the watermarking schemes used orthogonal wavelets. The bi-orthogonal wavelets
transform is an invertible transform. The property of symmetric wavelet functions and perfect reconstruction is
satisfied by bi-orthogonal wavelets, because they have two sets of high pass filters for decomposition and two
sets of low pass filters for reconstruction. One set is dual of the other. But in orthogonal wavelets it has only one
set. In bi-orthogonal wavelets the reconstruction and decomposition are obtained from two scaling functions
associated with two multi resolution analyses in duality. Bi-orthogonal wavelets have higher embedding
capacity if they are used to decompose image in to different channels. This is another advantage of bi-
orthogonal wavelets over orthogonal wavelets. In 1998 Hatzinakos and Kundur suggested a watermarking
technique based on bi-orthogonal wavelet transform which embeds a watermark in the detailed wavelet co-
efficients of the host image. The results they got was robust against many signal distortions.
2.3 Proposed method:
The objective of the proposed method is to present a imperceptible and robust watermarking scheme
which is based on bi-orthogonal wavelet transform. After performing 2d DWT the image will be decomposed as
we have discussed before. Among all the sub bands, the higher level sub bands are more significant when
compared to the lower level sub bands. The reason is, the higher level sub bands contain most of the energy co-
efficients, and therefore embedding the watermark in the higher level sub band will provide more
robustness. As the lower level sub bands have minor energy co-efficients, watermark embedded in these bands
are easily prone to attacks.The higher level approximation sub band i.e. LL2 sub band is not best for embedding
a watermark as it contains important information of the image. And it is also a low frequency band and can be
easily distorted. On the second level, considering diagonal sub band i.e. HH2 is not good of embedding
watermark as it can be easily be eliminated. If we do lossy compression of the image this sub band can be
eliminated as it has minor energy co-efficients. Therefore the middle frequency sub bands both vertical and
horizontal are best for embedding a watermark. The LH2 sub band contains more significant co-efficients
compared to HL2 sub band. For this reason, it‟s better to embed the watermark in the middle frequency band
LH2 instead of embedding the watermark in HL2.In the embedding process, the R, G and B channels of the
color host image has to be separated. Blue channel in particular is selected for embedding watermark because
this channel is more resistant to the changes done when compared to the other two channels red and green. And
other advantage is the human eye is less sensitive to the blue channel. An invisible watermark in the blue
channel can contain more energy than embedded in the luminance channel of the color host image.Blue channel
is decomposed into n-level by using bi-orthogonal wavelet transform. The property of symmetric wavelet
function and reconstruction exist in bi-orthogonal wavelet transform. Let us select us select bitmap image as
watermark image, which is of size 64*64. Then convert this watermark image into 1-D vector. Two PN
sequences are selected for embedding purpose i.e. 0 and 1 in the mid frequency sub bands of the higher level
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decomposition of the blue channel. Now using a additive watermarking technique for constructing the image as
follows
LH2‟ = LH2 + alpha * PN(0)
HL2‟ = HL2 + alpha * PN(1)
Where HL2‟ and LH‟2 are the watermarked sub bands and alpha is the embedding strength. The flow chart for
the embedding process of the watermark is as show below.
2.3 Performance evaluation:
Performance evaluation is very important in any project because it decides whether the project is
successful, efficient or not. For evaluating the performance I have calculated peak signal to noise ratio and
normalized cross correlation.Peak signal to noise ratio: Peak signal to noise ratio can be defined as the ratio
between the maximum possible power of the signal and the power of the noise that affects the signal‟s fidelity of
representation. It can be easily defined by mean squared error i.e. MSE. Let‟s take two m*n images „I‟ and „K‟
where one of them is considered as the noisy approximation of the other image. Then the equations for MSE and
Peak signal to noise ratio are as given below.
MSE =
PSNR (db) = 10 log 10 *
Normalized cross correlation: Normalized cross correlation usually denoted as NCCR is defined as the
correlation between the watermark image „W‟ and the extracted watermark „W‟”. After calculation if the value
of NC is nearer to 1 then W and W‟ are more similar to each other. Normalized correlation can be calculated
using the equation given below.
NC=
The peak signal to noise ratio and MSE values will be obtained using the original cover image with two
different watermarks i.e. text watermark and a logo watermark at different embedding strength alpha. I have
tested the performance of the proposed method by performing image processing operations such as adding
Gaussian noise and salt & pepper noise on the watermarked image. And I have tested it for various geometric
operations such as scaling, rotation as well as cropping.
III. RESULTS
Fig 3.1. Watermarked colored image
After getting the blue plane, we‟ll concatenate all red, green and blue planes to get back the color image with the
watermark embedded. The above showed figure is the watermarked color image.Peak signal to noise ratio is the
ratio between the maximum possible power of a signal and the power of corrupting noise the affects the fidelity
of its representation. After the watermark embedding process, we‟ll calculate the PSNR value is displayed as
show in the above figure.
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Fig 3.2 Graph of PSNR value v/s Alpha
The above figure is the graph showing PSNR value v/s the embedding factor alpha for both the text and logo
watermarks.
Fig 3.3 Graph of MSE v/s Alpha
The above figure is the graph showing MSE(Means square error) value v/s the embedding factor alpha for both
the text and logo watermarks
Fig 3.4 Graph of NCCR v/s alpha
The above figure is the graph showing NCCR (Normalized cross correlation) value v/s the embedding factor
alpha for both the text and logo watermarks.
Fig 3.5 Recovered text watermark
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The above figure is the decrypted text watermark which was embedded into the cover image
Fig 3.6 Recovered logo watermark
The above figure is the decrypted logo watermark which was embedded into the cover image. To check the
performance of the implemented method I have tried image processing operations on the watermarked image
such as adding salt & pepper as well as Gaussian noise to it. First we‟ll take the watermarked image either
watermarked by text or logo watermark, then salt and pepper noise will be added to that. The embedding factor
alpha should be fixed value 0.6 for both salt and pepper and Gaussian noise added.
Fig 3.7 Fixed embedding factor 0.6
Fig 3.8 Noise added watermarked image
The above figure is the salt and pepper noise added watermarked image.
Fig 3.9 Filtered watermarked image
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The noise added image is filtered by using median filter. That filtered image is as show in the figure.
Fig 3.10 Graph of Noise density v/s PSNR
The above figure is the graph showing PSNR value v/s the noise density for both the text and logo watermarks.
.
Fig 3.11 Graph of noise density v/s MSE
The above figure is the graph showing MSE value v/s the noise density for both the text and logo watermarks.
Fig 3.12 Graph of noise density v/s NCCR
The above figure is the graph showing NCCR value v/s the noise density for both the text and logo watermarks.
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Fig 3.13 Watermarked image for the alpha value 0.1
The above figure is the watermarked image got for the embedding factot alpha of 0.1, the PSNR valur got for
this is 67db and the MSE is 0.0045
Fig 3.14 Watermarked image for the alpha value 0.5
The above figure is the watermarke dimage got for the embedding factot alpha of 0.1, the PSNR valur got for
this is 48db and the MSE is 0.3702
Fig 3.15 Watermarked image for the alpha value 1.0
The above figure is the watermarke dimage got for the embedding factot alpha of 0.1, the PSNR value got for
this is 43db and the MSE is 1.3450
IV. CONCLUSION
In this project the implemented method used for embedding digital watermark is based on bi-
orthogonal wavelet transform. The watermark is embedded into the second level sub band of the discrete
wavelet transform decomposition. By using bi-orthogonal wavelets for decomposition, the distortion in the
watermarked image is very less compared to the Haar wavelet transform. The implemented method presents a
robust watermarking scheme. By using this technique I have tried to extract the watermark even if the
watermarked image is attacked. Primarily, the embedded watermark should not be responsible for the
degradation of the quality of the image. And it should be perceptually invisible for maintain the secrecy. Then,
the watermark must be robust enough to resist image processing attacks and should not be easily removable.
Only the owner of the image should be able to extract the embedded watermark.
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The performance of the proposed method is tested by applying various image processing and geometric attacks,
such as adding Gaussian, salt and pepper noise, scaling and cropping attack to the watermarked image. By
results obtained we got to know that when the alpha or embedding factor value increases the distortion in the
watermarked image also increases. And the exacted image quality also improves.
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