The document describes a technique for watermarking grayscale images using the least significant bit (LSB) method. It begins with an abstract that introduces digital watermarking and LSB watermarking. It then provides more details on the LSB algorithm and how it embeds a watermark by replacing the LSB of selected image pixels. The paper tests the technique on various images, embedding the watermark in different bit positions. It calculates the mean squared error and peak signal-to-noise ratio for the watermarked images. Finally, it applies different noise attacks to the watermarked images and measures the effect on quality.
Digital watermarking involves hiding identification information within digital content like images, video and audio. It can be used to authenticate ownership and detect unauthorized copying. There are two main types - visible watermarks which can be seen overlaid on an image, and invisible watermarks which are imperceptible but can be detected algorithmically. Digital watermarking works by imperceptibly altering content to embed a message. It has applications in copyright protection, content management, and tracking of digital media. The embedded data, or watermark, must be invisible, inseparable from the content, and not increase the file size. The Digital Watermarking Alliance promotes standards for watermarking across different media types.
Digital watermarking allows users to embed special patterns or data into digital content like images, audio, and video without changing the perceptual quality. Watermarking helps protect copyright ownership by embedding information directly into the media itself through small changes to the content data. Watermarks can be invisible, inseparable from the content after processing, and do not change the file size. Watermarks are classified based on human perception (visible or invisible), robustness (fragile, semi-fragile, or robust), and the type of document (text, image, audio, or video). Frequency domain techniques like discrete cosine transformation are commonly used to embed watermarks in images and videos.
With so much of our lives computerized, it is vitally important that machines and humans can understand one another and pass information back and forth. Mostly computers have things their way we have to & talk to them through relatively crude devices such as keyboards and mice so they can figure out what we want them to do. However, when it comes to processing more human kinds of information, like an old-fashioned printed book or a letter scribbled with a fountain pen, computers have to work much harder. That is where optical character recognition (OCR) comes in. Here we process the image, where we apply various pre-processing techniques like desk wing, binarization etc. and algorithms like Tesseract to recognize the characters and give us the final document. T.Gnana Prakash | K. Anusha"Text Extraction from Image using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2501.pdf http://www.ijtsrd.com/computer-science/simulation/2501/text-extraction-from-image-using-python/tgnana-prakash
This document provides an overview of steganography and watermarking techniques for hiding information in digital media. It defines steganography as "covered writing" involving hiding secret messages within other digital files like images, audio, or video. Common steganography methods embed data in the least significant bits of pixels or audio samples. Watermarking differs in embedding identifying marks that are robust to modifications and aim to protect copyrights. The document outlines various media and techniques for each, applications, advantages and limitations of both steganography and watermarking.
Digital watermarking involves imperceptibly embedding a watermark signal into digital content like images, video or audio. It can be used for copyright protection, content authentication, and metadata tagging. There are different types of watermarking including robust, fragile, visible, invisible, public and private watermarking. Common techniques include LSB watermarking and color separation. Digital watermarking has applications in copyright protection, online music industry, and news gathering using digital cameras.
Digital image watermarking is a technique to hide information (the watermark) within an image. It can be used for identification, authentication, and copyright protection. There are different domains to embed watermarks, including the spatial, wavelet, and frequency domains. The watermark is imperceptible, robust, inseparable from the image, and provides security. Watermarks can be extracted from the watermarked image after embedding.
Digital watermarking involves hiding identification information within digital content like images, video and audio. It can be used to authenticate ownership and detect unauthorized copying. There are two main types - visible watermarks which can be seen overlaid on an image, and invisible watermarks which are imperceptible but can be detected algorithmically. Digital watermarking works by imperceptibly altering content to embed a message. It has applications in copyright protection, content management, and tracking of digital media. The embedded data, or watermark, must be invisible, inseparable from the content, and not increase the file size. The Digital Watermarking Alliance promotes standards for watermarking across different media types.
Digital watermarking allows users to embed special patterns or data into digital content like images, audio, and video without changing the perceptual quality. Watermarking helps protect copyright ownership by embedding information directly into the media itself through small changes to the content data. Watermarks can be invisible, inseparable from the content after processing, and do not change the file size. Watermarks are classified based on human perception (visible or invisible), robustness (fragile, semi-fragile, or robust), and the type of document (text, image, audio, or video). Frequency domain techniques like discrete cosine transformation are commonly used to embed watermarks in images and videos.
With so much of our lives computerized, it is vitally important that machines and humans can understand one another and pass information back and forth. Mostly computers have things their way we have to & talk to them through relatively crude devices such as keyboards and mice so they can figure out what we want them to do. However, when it comes to processing more human kinds of information, like an old-fashioned printed book or a letter scribbled with a fountain pen, computers have to work much harder. That is where optical character recognition (OCR) comes in. Here we process the image, where we apply various pre-processing techniques like desk wing, binarization etc. and algorithms like Tesseract to recognize the characters and give us the final document. T.Gnana Prakash | K. Anusha"Text Extraction from Image using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2501.pdf http://www.ijtsrd.com/computer-science/simulation/2501/text-extraction-from-image-using-python/tgnana-prakash
This document provides an overview of steganography and watermarking techniques for hiding information in digital media. It defines steganography as "covered writing" involving hiding secret messages within other digital files like images, audio, or video. Common steganography methods embed data in the least significant bits of pixels or audio samples. Watermarking differs in embedding identifying marks that are robust to modifications and aim to protect copyrights. The document outlines various media and techniques for each, applications, advantages and limitations of both steganography and watermarking.
Digital watermarking involves imperceptibly embedding a watermark signal into digital content like images, video or audio. It can be used for copyright protection, content authentication, and metadata tagging. There are different types of watermarking including robust, fragile, visible, invisible, public and private watermarking. Common techniques include LSB watermarking and color separation. Digital watermarking has applications in copyright protection, online music industry, and news gathering using digital cameras.
Digital image watermarking is a technique to hide information (the watermark) within an image. It can be used for identification, authentication, and copyright protection. There are different domains to embed watermarks, including the spatial, wavelet, and frequency domains. The watermark is imperceptible, robust, inseparable from the image, and provides security. Watermarks can be extracted from the watermarked image after embedding.
This presentation features definition of watermarking, its applications, methods to implement a visible and invisible watermark and the possible attacks on watermark.
Steganography is the art and science of hiding data within other data. It works by embedding secret messages within images, audio files or other cover objects. Unlike cryptography, which encrypts messages to hide their meaning, steganography conceals the very existence of the message. Some key points about steganography include its Greek origins meaning "covered writing", the use of techniques like least significant bit insertion to hide data in image files, and its applications for copyright protection and transporting secret documents.
Digital watermarking involves embedding a hidden signal or watermark into digital content like images, audio or video. It can be used for copyright protection, content authentication and metadata tagging. There are different types of watermarking including robust, fragile, visible, invisible, public and private watermarking. Common techniques include LSB watermarking, color separation and bit stream watermarking. Digital watermarking faces attacks from techniques like Stirmark and mosaic attacks but continues to be an effective method for protecting digital multimedia content and verifying its authenticity.
Steganography is the practice of concealing a file, message, image, or video within another file, message, image, or video. The document discusses steganography techniques for hiding data in digital images. It describes how the least significant bit of pixels can be altered to embed hidden messages, leaving the image nearly indistinguishable to human eyes. An example algorithm is provided that replaces the least significant bit of each pixel with a bit from the secret message. Applications are discussed including secure file transfers and hidden communication between governments. The document also outlines pros and cons of steganography and summarizes how the students successfully created an application to encrypt and decrypt hidden messages in images using least significant bit substitution.
Digital watermarks are embedded signals or patterns inserted into digital media like text, images, or video that carry copyright information. There are various techniques for watermarking different types of media. Watermarking leaves the original file intact while encryption transforms the file contents. Popular watermarking applications include ownership assertion, fingerprinting to trace copies, authentication and integrity verification, content labeling, usage control, and content protection with visible watermarks. Watermarks should be detectable, unambiguous, and robust against attacks. Text watermarking alters spacing, images can modify pixel values in spatial or frequency domains, and checksum techniques embed a checksum in pixel bits. However, early watermarking schemes provided only limited protection against removal or forgery.
Digital watermarking is a technique for hiding copyright information in digital content such as images, audio and video. A digital watermark is imperceptibly embedded in the digital content and can be extracted or detected to prove ownership. There are two main types of watermarks - visible watermarks that can be seen and invisible watermarks that cannot be seen by the human eye. Watermarking techniques include spatial domain and frequency domain methods. The Fast Hadamard Transform is commonly used for digital image watermarking as it allows for faster processing times and robust watermarks. The watermarking process involves embedding, attacks on the watermarked content, and detection of the watermark.
This document discusses digital watermarking technology. It can be used to hide secret messages in computer files. Some applications of watermarking include rights management, content management, access control, and authentication. The document then describes different watermarking techniques such as spatial domain and frequency domain watermarking. It provides examples of least significant bit watermarking and discrete cosine transformation watermarking. The document also discusses important properties of watermarking like imperceptibility, robustness, capacity, and security.
This document provides an overview of digital watermarking. It begins with an introduction that defines digital watermarking as hiding information in digital media like images and video. The document then discusses the history of watermarking, which dates back over 700 years. It also covers the different types of watermarks, techniques, applications, and attributes of watermarking. In conclusion, it notes that watermarking technology has become widely used since the 1990s for purposes like copy prevention and data security.
The document summarizes a seminar presentation on steganography. It discusses the history of steganography from ancient Greece to modern digital techniques. It describes how steganography differs from cryptography in hiding information rather than encrypting it. The document outlines common steganography techniques like least significant bit insertion and the injection method. It provides examples of steganography applications for both legitimate uses like digital watermarking as well as illegitimate uses like corporate espionage. The presentation concludes that steganography can effectively hide sensitive information while cryptography provides additional security through encryption.
Data Compression, Lossy and Lossless Data Compression,Classification of Lossy and Lossless Data Compression, Huffman Codding method, LZW method of Lossless Compression and Compression Ratio
The document is a student project report on image steganography. It discusses using the least significant bit (LSB) method to hide information in digital images. The summary is:
1. It introduces steganography and LSB methods for hiding data in digital images by replacing the least significant bits of pixels.
2. Code is presented to embed a hidden message in an image by modifying pixels' LSBs and decoding the message from the stego image.
3. The report evaluates LSB steganography's advantages for covert communication but notes room for improving embedding capacity while maintaining secrecy.
Digital Watermarking describes methods and technologies that hide information, for example a number or text, in digital media, such as images, video. The embedding takes place by manipulating the content of the digital data, which means the information is not embedded in the frame around the data. The hiding process has to be such that the modifications of the media are imperceptible. For images this means that the modifications of the pixel values have to be invisible.
A digital watermark is a message which is embedded into digital content (video, images or text) that can be detected or extracted later. Moreover, in image the actual bits representing the watermark must be scattered throughout the file in such a way that they cannot be identified and manipulated. Watermarking is the insertion of imperceptible and inseparable information into the host data for data security & integrity. They are characterizing patterns, of varying visibility, added to the presentation media as a guarantee of authenticity, quality, ownership, and source. However, in digital watermarking, the message is supposed not to visible (or at least not interfering with the user experience of the content), but (only) electronic devices can retrieve the embedded message to identify the code. Another form of digital watermarking is known as steganography, in which a message is hidden in the content without typical citizens or the public authorities noticing its presence. Only a limited number of recipients can retrieve and decode the hidden message. Unlike a traditional watermark on paper, which is generally visible to the eye, digital watermarks can be made invisible or inaudible. They can, however, be read by a computer with the proper decoding software.
This document provides an overview of steganography, the art and science of hidden writing. It defines steganography as communicating in a way that hides the existence of a message. The document then discusses various digital and analog steganography techniques, including embedding messages in images, audio, video and other file types. It also covers the use of machine identification codes in printers, text encoding, and security schemes used to improve steganographic robustness.
This document provides an overview of a research project on image compression. It discusses image compression techniques including lossy and lossless compression. It describes using discrete wavelet transform, lifting wavelet transform, and stationary wavelet transform for image transformation. Experiments were conducted to compare the compression ratio and processing time of different combinations of wavelet transforms, vector quantization, and Huffman/Arithmetic coding. The results were analyzed to evaluate the compression performance and efficiency of the different methods.
This document provides an overview of steganography, including:
1) Steganography is the art of hiding information in plain sight so that the very existence of a hidden message is concealed. It works by embedding messages within images, audio, or other files.
2) Modern uses include digital watermarking to identify ownership, hiding sensitive files, and illegitimate uses like corporate espionage, terrorism, and child pornography.
3) Techniques include least significant bit insertion to replace bits in files, injection to directly embed messages, and generating new files from scratch. Detection methods like steganalysis aim to discover hidden information.
A PPT on Stegnography,
It describes the security of information via images by encrypting and decrypting it with the document.
Uses of different models and diagrams.
Steganography is the practice of concealing a file, message, image, or video within another file, message, image, or video. The word steganography combines the Greek words steganos meaning "covered, concealed, or protected", and graphein meaning "writing".
The first recorded use of the term was in 1499 by Johannes Trithemius in his Steganographia, a treatise on cryptography and steganography, disguised as a book on magic. Generally, the hidden messages appear to be (or be part of) something else: images, articles, shopping lists, or some other cover text. For example, the hidden message may be in invisible ink between the visible lines of a private letter. Some implementations of steganography that lack a shared secret are forms of security through obscurity, whereas key-dependent steganographic schemes adhere to Kerckhoffs's principle.
The advantage of steganography over cryptography alone is that the intended secret message does not attract attention to itself as an object of scrutiny. Plainly visible encrypted messages—no matter how unbreakable—arouse interest, and may in themselves be incriminating in countries where encryption is illegal.Thus, whereas cryptography is the practice of protecting the contents of a message alone, steganography is concerned with concealing the fact that a secret message is being sent, as well as concealing the contents of the message.
Steganography includes the concealment of information within computer files. In digital steganography, electronic communications may include steganographic coding inside of a transport layer, such as a document file, image file, program or protocol. Media files are ideal for steganographic transmission because of their large size. For example, a sender might start with an innocuous image file and adjust the color of every 100th pixel to correspond to a letter in the alphabet, a change so subtle that someone not specifically looking for it is unlikely to notice it.
This document discusses video watermarking. It introduces video watermarking, its need for copyright protection, and key terminologies. It describes two types of video watermarking - visible and invisible. Invisible watermarking has three sub-types. The document outlines desired properties, applications, and provides a generic approach involving insertion, detection, and removal of watermarks. It also discusses types of attacks on watermarks and concludes with future directions.
Bum Phillips invented a system for numbering defensive linemen alignments while coaching high school football. The system assigned numbers based on where linemen lined up in relation to offensive linemen prior to the snap. Although others have since modified the system slightly, it provided a universal way to communicate defensive line setups and ensured compatible positioning of linebackers.
Image Watermarking in Spatial Domain Using QIM and Genetic Algorithmijsrd.com
Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A watermark is a form of image or text that is impressed onto paper, which provides evidence of its authenticity. A digital watermark is digital data embedded in some host document so as to later prove the ownership of the document. Digital image watermarking refers to digital data embedding in images. Robust image watermarking systems are required so that watermarked images can resist geometric attacks in addition to common image processing tasks, such as JPEG compression. Least Significant Bit (LSB) watermarking, is one of the most traditional method of watermarking which changes the LSB of individual pixels in correlation with the watermark. However, pure LSB scheme provides a fragile watermarking technique which is not acceptable in practical applications. Also, robustness against geometric attacks, such as rotation, scaling and translation, still remains one of the most challenging research topics in pixel based image watermarking. In this paper, a new pixel-based watermarking system is proposed, in which a binary logo is embedded, a bit per pixel, in the pixel domain of an image. The LSB based watermarking is then quantized using QIM, augmented with genetic algorithm to produce a watermarking scheme which is highly robust against geometrical attacks.
Lsb hiding using random approach for image watermarkingeSAT Journals
Abstract A digital image watermarking is the process of embedding an image with a secondary parameter called watermark, without deterioration in the quality of image to provide copyright protection means to provide protection for intellectual property from illegal copying. In this paper the method of nested digital image watermarking is used that means a watermark inside another watermark embedded into the cover image that is the main image. Here the Randomized LSB hiding algorithm is used for embedding one image into another as it has lesser complexity and the approach is more robust to the variations in the type of image. The blowfish algorithm is used to encrypt the watermark image before embedding into the cover image. The concept of encryption of watermark image before get embedded into the main image is used here to increase the security of the watermark image. This is because the research work is mainly focus on to get the more secured watermark by improving and enhancing the embedding capacity. Key Words: Digital image Watermarking, Randomized LSB, Blowfish, Copyright Protection
This presentation features definition of watermarking, its applications, methods to implement a visible and invisible watermark and the possible attacks on watermark.
Steganography is the art and science of hiding data within other data. It works by embedding secret messages within images, audio files or other cover objects. Unlike cryptography, which encrypts messages to hide their meaning, steganography conceals the very existence of the message. Some key points about steganography include its Greek origins meaning "covered writing", the use of techniques like least significant bit insertion to hide data in image files, and its applications for copyright protection and transporting secret documents.
Digital watermarking involves embedding a hidden signal or watermark into digital content like images, audio or video. It can be used for copyright protection, content authentication and metadata tagging. There are different types of watermarking including robust, fragile, visible, invisible, public and private watermarking. Common techniques include LSB watermarking, color separation and bit stream watermarking. Digital watermarking faces attacks from techniques like Stirmark and mosaic attacks but continues to be an effective method for protecting digital multimedia content and verifying its authenticity.
Steganography is the practice of concealing a file, message, image, or video within another file, message, image, or video. The document discusses steganography techniques for hiding data in digital images. It describes how the least significant bit of pixels can be altered to embed hidden messages, leaving the image nearly indistinguishable to human eyes. An example algorithm is provided that replaces the least significant bit of each pixel with a bit from the secret message. Applications are discussed including secure file transfers and hidden communication between governments. The document also outlines pros and cons of steganography and summarizes how the students successfully created an application to encrypt and decrypt hidden messages in images using least significant bit substitution.
Digital watermarks are embedded signals or patterns inserted into digital media like text, images, or video that carry copyright information. There are various techniques for watermarking different types of media. Watermarking leaves the original file intact while encryption transforms the file contents. Popular watermarking applications include ownership assertion, fingerprinting to trace copies, authentication and integrity verification, content labeling, usage control, and content protection with visible watermarks. Watermarks should be detectable, unambiguous, and robust against attacks. Text watermarking alters spacing, images can modify pixel values in spatial or frequency domains, and checksum techniques embed a checksum in pixel bits. However, early watermarking schemes provided only limited protection against removal or forgery.
Digital watermarking is a technique for hiding copyright information in digital content such as images, audio and video. A digital watermark is imperceptibly embedded in the digital content and can be extracted or detected to prove ownership. There are two main types of watermarks - visible watermarks that can be seen and invisible watermarks that cannot be seen by the human eye. Watermarking techniques include spatial domain and frequency domain methods. The Fast Hadamard Transform is commonly used for digital image watermarking as it allows for faster processing times and robust watermarks. The watermarking process involves embedding, attacks on the watermarked content, and detection of the watermark.
This document discusses digital watermarking technology. It can be used to hide secret messages in computer files. Some applications of watermarking include rights management, content management, access control, and authentication. The document then describes different watermarking techniques such as spatial domain and frequency domain watermarking. It provides examples of least significant bit watermarking and discrete cosine transformation watermarking. The document also discusses important properties of watermarking like imperceptibility, robustness, capacity, and security.
This document provides an overview of digital watermarking. It begins with an introduction that defines digital watermarking as hiding information in digital media like images and video. The document then discusses the history of watermarking, which dates back over 700 years. It also covers the different types of watermarks, techniques, applications, and attributes of watermarking. In conclusion, it notes that watermarking technology has become widely used since the 1990s for purposes like copy prevention and data security.
The document summarizes a seminar presentation on steganography. It discusses the history of steganography from ancient Greece to modern digital techniques. It describes how steganography differs from cryptography in hiding information rather than encrypting it. The document outlines common steganography techniques like least significant bit insertion and the injection method. It provides examples of steganography applications for both legitimate uses like digital watermarking as well as illegitimate uses like corporate espionage. The presentation concludes that steganography can effectively hide sensitive information while cryptography provides additional security through encryption.
Data Compression, Lossy and Lossless Data Compression,Classification of Lossy and Lossless Data Compression, Huffman Codding method, LZW method of Lossless Compression and Compression Ratio
The document is a student project report on image steganography. It discusses using the least significant bit (LSB) method to hide information in digital images. The summary is:
1. It introduces steganography and LSB methods for hiding data in digital images by replacing the least significant bits of pixels.
2. Code is presented to embed a hidden message in an image by modifying pixels' LSBs and decoding the message from the stego image.
3. The report evaluates LSB steganography's advantages for covert communication but notes room for improving embedding capacity while maintaining secrecy.
Digital Watermarking describes methods and technologies that hide information, for example a number or text, in digital media, such as images, video. The embedding takes place by manipulating the content of the digital data, which means the information is not embedded in the frame around the data. The hiding process has to be such that the modifications of the media are imperceptible. For images this means that the modifications of the pixel values have to be invisible.
A digital watermark is a message which is embedded into digital content (video, images or text) that can be detected or extracted later. Moreover, in image the actual bits representing the watermark must be scattered throughout the file in such a way that they cannot be identified and manipulated. Watermarking is the insertion of imperceptible and inseparable information into the host data for data security & integrity. They are characterizing patterns, of varying visibility, added to the presentation media as a guarantee of authenticity, quality, ownership, and source. However, in digital watermarking, the message is supposed not to visible (or at least not interfering with the user experience of the content), but (only) electronic devices can retrieve the embedded message to identify the code. Another form of digital watermarking is known as steganography, in which a message is hidden in the content without typical citizens or the public authorities noticing its presence. Only a limited number of recipients can retrieve and decode the hidden message. Unlike a traditional watermark on paper, which is generally visible to the eye, digital watermarks can be made invisible or inaudible. They can, however, be read by a computer with the proper decoding software.
This document provides an overview of steganography, the art and science of hidden writing. It defines steganography as communicating in a way that hides the existence of a message. The document then discusses various digital and analog steganography techniques, including embedding messages in images, audio, video and other file types. It also covers the use of machine identification codes in printers, text encoding, and security schemes used to improve steganographic robustness.
This document provides an overview of a research project on image compression. It discusses image compression techniques including lossy and lossless compression. It describes using discrete wavelet transform, lifting wavelet transform, and stationary wavelet transform for image transformation. Experiments were conducted to compare the compression ratio and processing time of different combinations of wavelet transforms, vector quantization, and Huffman/Arithmetic coding. The results were analyzed to evaluate the compression performance and efficiency of the different methods.
This document provides an overview of steganography, including:
1) Steganography is the art of hiding information in plain sight so that the very existence of a hidden message is concealed. It works by embedding messages within images, audio, or other files.
2) Modern uses include digital watermarking to identify ownership, hiding sensitive files, and illegitimate uses like corporate espionage, terrorism, and child pornography.
3) Techniques include least significant bit insertion to replace bits in files, injection to directly embed messages, and generating new files from scratch. Detection methods like steganalysis aim to discover hidden information.
A PPT on Stegnography,
It describes the security of information via images by encrypting and decrypting it with the document.
Uses of different models and diagrams.
Steganography is the practice of concealing a file, message, image, or video within another file, message, image, or video. The word steganography combines the Greek words steganos meaning "covered, concealed, or protected", and graphein meaning "writing".
The first recorded use of the term was in 1499 by Johannes Trithemius in his Steganographia, a treatise on cryptography and steganography, disguised as a book on magic. Generally, the hidden messages appear to be (or be part of) something else: images, articles, shopping lists, or some other cover text. For example, the hidden message may be in invisible ink between the visible lines of a private letter. Some implementations of steganography that lack a shared secret are forms of security through obscurity, whereas key-dependent steganographic schemes adhere to Kerckhoffs's principle.
The advantage of steganography over cryptography alone is that the intended secret message does not attract attention to itself as an object of scrutiny. Plainly visible encrypted messages—no matter how unbreakable—arouse interest, and may in themselves be incriminating in countries where encryption is illegal.Thus, whereas cryptography is the practice of protecting the contents of a message alone, steganography is concerned with concealing the fact that a secret message is being sent, as well as concealing the contents of the message.
Steganography includes the concealment of information within computer files. In digital steganography, electronic communications may include steganographic coding inside of a transport layer, such as a document file, image file, program or protocol. Media files are ideal for steganographic transmission because of their large size. For example, a sender might start with an innocuous image file and adjust the color of every 100th pixel to correspond to a letter in the alphabet, a change so subtle that someone not specifically looking for it is unlikely to notice it.
This document discusses video watermarking. It introduces video watermarking, its need for copyright protection, and key terminologies. It describes two types of video watermarking - visible and invisible. Invisible watermarking has three sub-types. The document outlines desired properties, applications, and provides a generic approach involving insertion, detection, and removal of watermarks. It also discusses types of attacks on watermarks and concludes with future directions.
Bum Phillips invented a system for numbering defensive linemen alignments while coaching high school football. The system assigned numbers based on where linemen lined up in relation to offensive linemen prior to the snap. Although others have since modified the system slightly, it provided a universal way to communicate defensive line setups and ensured compatible positioning of linebackers.
Image Watermarking in Spatial Domain Using QIM and Genetic Algorithmijsrd.com
Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A watermark is a form of image or text that is impressed onto paper, which provides evidence of its authenticity. A digital watermark is digital data embedded in some host document so as to later prove the ownership of the document. Digital image watermarking refers to digital data embedding in images. Robust image watermarking systems are required so that watermarked images can resist geometric attacks in addition to common image processing tasks, such as JPEG compression. Least Significant Bit (LSB) watermarking, is one of the most traditional method of watermarking which changes the LSB of individual pixels in correlation with the watermark. However, pure LSB scheme provides a fragile watermarking technique which is not acceptable in practical applications. Also, robustness against geometric attacks, such as rotation, scaling and translation, still remains one of the most challenging research topics in pixel based image watermarking. In this paper, a new pixel-based watermarking system is proposed, in which a binary logo is embedded, a bit per pixel, in the pixel domain of an image. The LSB based watermarking is then quantized using QIM, augmented with genetic algorithm to produce a watermarking scheme which is highly robust against geometrical attacks.
Lsb hiding using random approach for image watermarkingeSAT Journals
Abstract A digital image watermarking is the process of embedding an image with a secondary parameter called watermark, without deterioration in the quality of image to provide copyright protection means to provide protection for intellectual property from illegal copying. In this paper the method of nested digital image watermarking is used that means a watermark inside another watermark embedded into the cover image that is the main image. Here the Randomized LSB hiding algorithm is used for embedding one image into another as it has lesser complexity and the approach is more robust to the variations in the type of image. The blowfish algorithm is used to encrypt the watermark image before embedding into the cover image. The concept of encryption of watermark image before get embedded into the main image is used here to increase the security of the watermark image. This is because the research work is mainly focus on to get the more secured watermark by improving and enhancing the embedding capacity. Key Words: Digital image Watermarking, Randomized LSB, Blowfish, Copyright Protection
This document discusses different types of counters, including asynchronous and synchronous counters. Asynchronous counters use flip-flops that are not connected to a common clock, resulting in a "ripple" effect. Synchronous counters connect all flip-flops to the same clock and use combinational logic to generate the next state. Counters can be cascaded to achieve higher modulus by connecting the output of one counter to the input of the next. The document also provides an example of designing a synchronous BCD counter and cascading a mod-10 and mod-8 counter.
Operating system introduction to operating systemjaydeesa17
This document introduces operating systems and their history. It defines an operating system as software that manages computer hardware and provides a simpler interface for user programs. Operating systems are discussed from the user and system perspectives. The history of operating systems is covered in generations from vacuum tubes to personal computers. Three main types of operating systems are described: batch, multiprogramming, and multi-user. Batch systems ran jobs in batches while the other two allowed more concurrent usage of hardware through time-sharing and memory sharing.
Digital watermarking techniques can be used to hide copyright information in digital media such as images, audio, and video. There are three main phases in a digital watermarking system: embedding, where the watermark is hidden in the cover media; attacks, where the watermarked content may be modified; and extraction, where the watermark is detected. Watermarking techniques can be classified based on various parameters such as whether they produce robust or fragile watermarks, the domain in which embedding occurs (spatial or frequency), and whether keys are required for embedding and detection. Common spatial domain techniques include least significant bit embedding and spread spectrum modulation, while frequency domain techniques operate in the discrete cosine transform or discrete wavelet transform domains.
This document discusses steganography and image steganography techniques. It defines steganography as hiding information within other information to avoid detection. Image steganography is described as hiding data in digital images using techniques like least significant bit encoding. The document outlines the LSB algorithm, which replaces the least significant bits of image pixel values with bits of the hidden message. Examples are given to illustrate how short messages can be concealed in an image using this method.
Digital watermarking embeds invisible identifying information into digital content like audio, video, and images. This allows copyright owners to establish ownership and prevents unauthorized distribution even if the content is copied. There are different types of watermarks that can be embedded, including robust watermarks that are difficult to remove and fragile watermarks that are destroyed if the content is tampered with. However, watermarks are not impossible to attack, as image processing techniques can potentially disable or overwrite them.
Digital video watermarking using modified lsb and dct techniqueeSAT 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
The document provides an overview of operating system concepts, including:
- The four main components of a computer system: hardware, operating system, applications, and users.
- What operating systems do, such as managing resources and controlling programs.
- Computer system organization involving CPUs, memory, I/O devices, and interrupts.
- Operating system structures like processes, memory management, and storage management.
IC 4017 is a 16 Pin Decade counter, used to produce decoded decimal count as output. Find a couple of applications like circling LEDs and running light.
The document discusses the functions and types of operating systems. It defines an operating system as the most important program that runs on a computer and performs basic tasks like recognizing input/output and managing files. The major functions of an operating system are providing an interface for users, managing system resources like memory and CPU time, running applications, and handling security and access rights. The document outlines different types of operating systems including real-time, single-user/multi-tasking, multi-user, distributed, and embedded operating systems. Examples of specific operating systems are also provided.
This document describes different types of synchronous counters that are triggered by a common clock signal. It discusses binary counters that count up in binary, up-down binary counters that can count up or down, BCD counters that count in decimal, and binary counters that can be parallel loaded. The key aspects covered are how the flip-flops are triggered in each counter type and the inputs and functions that control counting direction, clearing, loading, and counting.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
This document provides information about different types of counters, including asynchronous counters, synchronous counters, MSI counters, and specific counter integrated circuits. It defines counters and describes their basic characteristics. It discusses asynchronous ripple counters and their timing. It provides examples of decade and binary counters. It describes synchronous counters and MSI counters like the 74LS163 4-bit synchronous counter. Finally, it provides truth tables, logic diagrams, and application information for common counter ICs like the 7490, 7492, 7493, and 74LS163.
This document discusses different types of counters used in digital circuits. It defines a counter as a sequential circuit that cycles through a sequence of states in response to clock pulses. Binary counters count in binary and can count from 0 to 2n-1 with n flip-flops. Asynchronous counters have flip-flops that are not triggered simultaneously by a clock, while synchronous counters use a common clock for all flip-flops. Other counter types include ring counters, Johnson counters, and decade counters. The document provides examples of binary, asynchronous, and synchronous counters and discusses their applications in areas like timing sequences and addressing memory.
Counters are sequential circuits where the output value increases by one on each clock cycle and wraps around back to zero after reaching the maximum value. Ripple counters are asynchronous counters where the flip-flops are not connected to a common clock, but instead are triggered one after another by each previous flip-flop's output in a ripple effect. This causes the periods of each successive flip-flop to be multiples of the clock period, with the last flip-flop having the largest period of 2n for an n-bit ripple counter.
1. A counter is a sequential logic circuit consisting of a set of flip-flops which can go through a sequence of states.
2. There are two main types of counters - asynchronous counters and synchronous counters. Asynchronous counters have propagation delay issues and synchronous counters do not.
3. Counters can be designed to count up, down, or in other sequences depending on the state transition logic and excitation table used to determine the flip-flop inputs.
The document discusses synchronous and asynchronous counters. It begins by explaining the difference between synchronous and asynchronous counters. Asynchronous counters have the clock signal applied to only the first flip-flop, while synchronous counters have the clock applied to all flip-flops simultaneously. The document then discusses various types of counters like up counters, down counters, decade counters, and up-down counters. It provides circuit diagrams and timing diagrams to illustrate the operation of these counters. It also discusses using integrated circuits like the 74293 to implement asynchronous counters of different moduli. Finally, it notes some disadvantages of asynchronous counters and why synchronous counters are preferable.
Nowadays, digital watermarking has many
applications such as broadcast monitoring, owner identification,
proof of ownership, transaction tracking. Embedding a hidden
stream of bits in a file is called Digital Watermarking. This paper
introduces a LSB information hiding algorithm which can lift the
wavelet transform image. LSB based Steganography embeds the
hiding text message in least significant bit of the pixels. The
proposed method has good invisibility, robustness for a lot of
hidden attacks. As we think about the capacity lead us to think
about improved approach which can be achieved through
hardware implementation system by using Field Programmable
Gate Array (FPGA). In this paper hardware implementation of
digital watermarking system is proposed. MATLAB is used to
convert images into pixel-format files and to observe simulation
results. To implement this paper XPS & VB are needed. In XPS,
first select hardware & software components then by adding
source and header files & converting into bit streams and
download into FPGA, to obtain Stego image.
INFORMATION SECURITY THROUGH IMAGE WATERMARKING USING LEAST SIGNIFICANT BIT A...cscpconf
The rapid advancement of internet has made it easier to send the data/image accurate and
faster to the destination. Besides this, it is easier to modify and misuse the valuable information
through hacking at the same time. In order to transfer the data/image securely to the destination
without any modifications, there are many approaches like Cryptography, Watermarking and
Steganography. This paper presents the general overview of image watermarking and different
security issues. In this paper, Image Watermarking using Least Significant Bit (LSB) algorithm
has been used for embedding the message/logo into the image. This work has been implemented
through MATLAB
A Review of BSS Based Digital Image Watermarking and Extraction MethodsIOSR Journals
This document provides a review of blind source separation (BSS) based digital image watermarking and extraction methods. It begins with an introduction to BSS and its application in digital image watermarking. It then surveys various digital image watermarking methods and BSS techniques used for watermark embedding and extraction. The document discusses the general watermarking framework including embedding, attacks, and detection. It also explores challenges in digital image watermarking such as capacity, robustness, and transparency. Finally, it concludes that further research can improve BSS-based watermarking methods to achieve high imperceptibility and robustness.
A Review of BSS Based Digital Image Watermarking and Extraction MethodsIOSR Journals
Abstract :The field of Signal Processing has witnessed the strong emergence of a new technique, the Blind Signal Processing (BSP) which is based on sound theoretical foundation. An offshoot of the BSP is known as Blind Source Separation (BSS). This digital signal processing techniques have a wide and varied potential applications. The term blind is indicative of the fact that both the source signal and the mixing procedures are unknown. One of the more interesting applications of BSS is in field of image data security/authentication where digital watermarking is proposed. Watermarking is a promising technique to help protect data security and intellectual property rights. The plethora digital image watermarking methods are surveyed and discussed here with their features and limitations. Thus literature survey is presented in two major categories-Digital image watermarking methods and BSS based techniques in digital image watermarking and extraction. Keywords – BSP, BSS, Mixing Coefficient, Digital Image Watermarking, Watermark Extraction.
This document discusses a new approach to providing secure data transmission that combines digital watermarking and image compression techniques. Digital watermarking involves embedding hidden information in multimedia content like images, audio or video. The proposed approach uses discrete cosine transform (DCT) based watermarking combined with an improved adaptive Huffman encoding image compression algorithm. This combined technique aims to enhance security for data transmission while reducing storage space requirements compared to other compression methods.
This document discusses a proposed technique for secure data transmission that combines digital image watermarking and image compression. It begins with background information on digital watermarking, including its classifications, requirements, general system, and techniques such as spatial domain and frequency domain methods. It then provides an overview of image compression, including its benefits, techniques such as lossless and lossy compression, and common compression methods. The proposed technique embeds a watermark into an image using discrete cosine transform (DCT) based watermarking in the frequency domain. It then applies lossy image compression to the watermarked image using an improved adaptive Huffman coding algorithm. The goal is to achieve higher security for data transmission by combining these two techniques compared to
Performance Comparison of Digital Image Watermarking Techniques: A SurveyEditor IJCATR
Digital watermarking is the processing of combined information into a digital signal. A watermark is a secondary image,
which is overlaid on the host image, and provides a means of protecting the image. In order to provide high quality watermarked
image, the watermarked image should be imperceptible. This paper presents different techniques of digital image watermarking based
on spatial & frequency domain, which shows that spatial domain technique provides security & successful recovery of watermark
image and higher PSNR value compared to frequency domain.
This document summarizes a student project on reversible data hiding techniques. The project compares different reversible watermarking methods and proposes a new technique that embeds a secret bitstream into a color image using bisection and square root interpolation. Experimental results showed the embedded and extracted bitstreams had a correlation of 1, indicating no data loss. Future work could improve the algorithm security by using multiple color planes and transformations for watermarking.
ANALYSIS OF IMAGE WATERMARKING USING LEAST SIGNIFICANT BIT ALGORITHMijistjournal
The rapid advancement of internet has made it easier to send the data/image accurate and faster to the destination. But thisadvantage is also accompanied with the disadvantage of modifying and misusing the valuable information through intercepting or hacking.So In order to transfer the data/image to the intended user at destination without anyalterations or modifications, there are many approaches like Cryptography, Watermarking and Steganography. This paper presents the general overview of image watermarking and different security issues. In this paper, Image Watermarking using Least Significant Bit (LSB) algorithm has been used for embedding the message/logo into the image. This work has been implemented through MATLAB.
ANALYSIS OF IMAGE WATERMARKING USING LEAST SIGNIFICANT BIT ALGORITHMijistjournal
The rapid advancement of internet has made it easier to send the data/image accurate and faster to the destination. But thisadvantage is also accompanied with the disadvantage of modifying and misusing the valuable information through intercepting or hacking.So In order to transfer the data/image to the intended user at destination without anyalterations or modifications, there are many approaches like Cryptography, Watermarking and Steganography. This paper presents the general overview of image watermarking and different security issues. In this paper, Image Watermarking using Least Significant Bit (LSB) algorithm has been used for embedding the message/logo into the image. This work has been implemented through MATLAB.
This document provides an overview of digital watermarking techniques. It discusses how watermarking has evolved from earlier steganography methods and classifications of watermarking such as image, audio, and video watermarking. It also summarizes various watermarking techniques including spatial domain methods that directly modify pixel values, frequency domain methods that operate in transform domains like DCT and DWT, and spread spectrum techniques. Specific spatial and frequency domain techniques are described for image, audio, and video watermarking. The document concludes that watermarking continues to be an evolving topic with opportunities remaining to further develop fragile and semi-fragile techniques.
A Review on Robust Digital Watermarking based on different Methods and its Ap...IJSRD
Digital Watermarking is the process of embedding data called watermark or signature or label or tag into a multimedia object (image or audio or video) so that the watermark can be extracted for ownership verification or authentication. A visible watermark is a secondary translucent image overlaid into the primary image and appears visible to a viewer on a careful inspection. The invisible watermark is embedded in such a way that the modification made to the pixel value is perceptually not noticed and it can be recovered only with an appropriate decoding mechanism. Digital watermarking is used to hide the information inside a signal, which cannot be easily extracted by the third party. Its widely used application is copyright protection of digital information. It is different from the encryption in the sense that it allows the user to access, view and interpret the signal but protect the ownership of the content. One of the current research areas is to protect digital watermark inside the information so that ownership of the information cannot be claimed by third party.
Digital watermarking knowledge is a leading edge research field and it mainly focuses on the
intellectual property rights, hides data and embedded inside an image to show authenticity or proof
of ownership, discovery and authentication of the digital media to protect the important documents.
Digital watermarking can help to verify ownership, to recognize a misappropriate person and find the
marked documents. One of the significant technological actions of the last two decades was the
attack of digital media in a complete range of everyday life aspects.
Digital data can be stored efficiently with a very high quality and it can be manipulated very
easily using computers. In addition digital data can be transmitted in a fast and inexpensive way
through data communication networks without losing quality. According to the necessary study of
digital image watermarking, the digital watermarking model consists of two modules, which are
watermark embedding module and watermark extraction and detection module.
A Survey on Features Combination for Image WatermarkingEditor IJMTER
As the internet users are increasing day by day it is easy to transfer digital data. By this new
problem of data piracy is increasing. For this different methods of watermarking are developed for
protecting the digital data like video, audio, image, etc. Out of these many researcher are working on
image watermarking field from last few decades. This paper focus on the image watermarking features
combination with various techniques which are broadly categorized into spatial and frequency domain.
Many features are studied with their different requirement and functionality. It has been observed that
most of the researcher combines many features for achieving the prior goal of the watermark that is to
embed watermark and extract from the carrier image in presence of different attack.
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.
PREVENTING COPYRIGHTS INFRINGEMENT OF IMAGES BY WATERMARKING IN TRANSFORM DOM...ijistjournal
1) The document discusses a method for preventing copyright infringement of images using watermarking in the transform domain and a full counter propagation neural network.
2) It aims to encode the host image before watermark embedding to enhance security. The fast and effective full counter propagation neural network then helps successfully embed the watermark without deteriorating the image quality.
3) Previous techniques embedded watermarks directly in images, but the authors find neural network synapses provide a better way to reduce distortion and increase message capacity when embedding watermarks.
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.
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.
This document discusses a randomized LSB hiding approach for nested digital image watermarking. It proposes encrypting one watermark image using Blowfish before embedding it into another watermark image using randomized LSB hiding. This nested watermark is then encrypted again using Blowfish before being embedded into the cover image for increased security. Randomized LSB hiding is used for embedding as it has lower complexity and is more robust than direct LSB hiding. The approach aims to improve security and embedding capacity for copyright protection of digital images.
STAGE STAFFING SCHEME FOR COPYRIGHT PROTECTION IN MULTIMEDIAIJNSA Journal
Copyright protection has become a need in today’s world. To achieve a secure copyright protection we embedded some information in images and videos and that image or video is called copyright protected. The embedded information can’t be detected by human eye but some attacks and operations can tamper that information to breach protection. So in order to find a secure technique of copyright protection, we have analyzed image processing techniques i.e. Spatial Domain (Least Significant Bit (LSB)), Transform Domain (Discrete Cosine Transform (DCT)), Discrete Wavelet Transform (DWT) and there are numerous algorithm for watermarking using them. After having a good understanding of the same we have proposed a novel algorithm named as Stage Staffing Algorithm that generates results with high effectiveness, additionally we can use self extracted-watermark technique to increase the security and automate the process of watermark image. The proposed algorithm provides protection in three stages. We have implemented the algorithm and results of the simulations are shown. The various factors affecting spatial domain watermarking are also discussed.
Abstract: The increasing amount of applications using digital multimedia technologies has accentuated the need to provide copyright protection to multimedia data. This paper reviews one of the data hiding techniques - digital image watermarking. Through this paper we will explore some basic concepts of digital image watermarking techniques.Two different methods of digital image watermarking namely spatial domain watermarking and transform domain watermarking are briefly discussed in this paper. Furthermore, two different algorithms for a digital image watermarking have also been discussed. Also the comparision between the different algorithms,tests performed for the robustness and the applications of the digital image watermarking have also been discussed.
Similar to Lsb Based Digital Image Watermarking For Gray Scale Image (20)
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
This document analyzes the performance of various modulation schemes for achieving energy efficient communication over fading channels in wireless sensor networks. It finds that for long transmission distances, low-order modulations like BPSK are optimal due to their lower SNR requirements. However, as transmission distance decreases, higher-order modulations like 16-QAM and 64-QAM become more optimal since they can transmit more bits per symbol, outweighing their higher SNR needs. Simulations show lifetime extensions up to 550% are possible in short-range networks by using higher-order modulations instead of just BPSK. The optimal modulation depends on transmission distance and balancing the energy used by electronic components versus power amplifiers.
This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
1) The document simulates and compares the performance of AODV and DSDV routing protocols in a mobile ad hoc network under three conditions: when users are fixed, when users move towards the base station, and when users move away from the base station.
2) The results show that both protocols have higher packet delivery and lower packet loss when users are either fixed or moving towards the base station, since signal strength is better in those scenarios. Performance degrades when users move away from the base station due to weaker signals.
3) AODV generally has better performance than DSDV, with higher throughput and packet delivery rates observed across the different user mobility conditions.
This document describes the design and implementation of 4-bit QPSK and 256-bit QAM modulation techniques using MATLAB. It compares the two techniques based on SNR, BER, and efficiency. The key steps of implementing each technique in MATLAB are outlined, including generating random bits, modulation, adding noise, and measuring BER. Simulation results show scatter plots and eye diagrams of the modulated signals. A table compares the results, showing that 256-bit QAM provides better performance than 4-bit QPSK. The document concludes that QAM modulation is more effective for digital transmission systems.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
This document studies the effects of dielectric superstrate thickness on microstrip patch antenna parameters. Three types of probes-fed patch antennas (rectangular, circular, and square) were designed to operate at 2.4 GHz using Arlondiclad 880 substrate. The antennas were tested with and without an Arlondiclad 880 superstrate of varying thicknesses. It was found that adding a superstrate slightly degraded performance by lowering the resonant frequency and increasing return loss and VSWR, while decreasing bandwidth and gain. Specifically, increasing the superstrate thickness or dielectric constant resulted in greater changes to the antenna parameters.
This document describes a wireless environment monitoring system that utilizes soil energy as a sustainable power source for wireless sensors. The system uses a microbial fuel cell to generate electricity from the microbial activity in soil. Two microbial fuel cells were created using different soil types and various additives to produce different current and voltage outputs. An electronic circuit was designed on a printed circuit board with components like a microcontroller and ZigBee transceiver. Sensors for temperature and humidity were connected to the circuit to monitor the environment wirelessly. The system provides a low-cost way to power remote sensors without needing battery replacement and avoids the high costs of wiring a power source.
1) The document proposes a model for a frequency tunable inverted-F antenna that uses ferrite material.
2) The resonant frequency of the antenna can be significantly shifted from 2.41GHz to 3.15GHz, a 31% shift, by increasing the static magnetic field placed on the ferrite material.
3) Altering the permeability of the ferrite allows tuning of the antenna's resonant frequency without changing the physical dimensions, providing flexibility to operate over a wide frequency range.
This document summarizes a research paper that presents a speech enhancement method using stationary wavelet transform. The method first classifies speech into voiced, unvoiced, and silence regions based on short-time energy. It then applies different thresholding techniques to the wavelet coefficients of each region - modified hard thresholding for voiced speech, semi-soft thresholding for unvoiced speech, and setting coefficients to zero for silence. Experimental results using speech from the TIMIT database corrupted with white Gaussian noise at various SNR levels show improved performance over other popular denoising methods.
This document reviews the design of an energy-optimized wireless sensor node that encrypts data for transmission. It discusses how sensing schemes that group nodes into clusters and transmit aggregated data can reduce energy consumption compared to individual node transmissions. The proposed node design calculates the minimum transmission power needed based on received signal strength and uses a periodic sleep/wake cycle to optimize energy when not sensing or transmitting. It aims to encrypt data at both the node and network level to further optimize energy usage for wireless communication.
This document discusses group consumption modes. It analyzes factors that impact group consumption, including external environmental factors like technological developments enabling new forms of online and offline interactions, as well as internal motivational factors at both the group and individual level. The document then proposes that group consumption modes can be divided into four types based on two dimensions: vertical (group relationship intensity) and horizontal (consumption action period). These four types are instrument-oriented, information-oriented, enjoyment-oriented, and relationship-oriented consumption modes. Finally, the document notes that consumption modes are dynamic and can evolve over time.
The document summarizes a study of different microstrip patch antenna configurations with slotted ground planes. Three antenna designs were proposed and their performance evaluated through simulation: a conventional square patch, an elliptical patch, and a star-shaped patch. All antennas were mounted on an FR4 substrate. The effects of adding different slot patterns to the ground plane on resonance frequency, bandwidth, gain and efficiency were analyzed parametrically. Key findings were that reshaping the patch and adding slots increased bandwidth and shifted resonance frequency. The elliptical and star patches in particular performed better than the conventional design. Three antenna configurations were selected for fabrication and measurement based on the simulations: a conventional patch with a slot under the patch, an elliptical patch with slots
1) The document describes a study conducted to improve call drop rates in a GSM network through RF optimization.
2) Drive testing was performed before and after optimization using TEMS software to record network parameters like RxLevel, RxQuality, and events.
3) Analysis found call drops were occurring due to issues like handover failures between sectors, interference from adjacent channels, and overshooting due to antenna tilt.
4) Corrective actions taken included defining neighbors between sectors, adjusting frequencies to reduce interference, and lowering the mechanical tilt of an antenna.
5) Post-optimization drive testing showed improvements in RxLevel, RxQuality, and a reduction in dropped calls.
This document describes the design of an intelligent autonomous wheeled robot that uses RF transmission for communication. The robot has two modes - automatic mode where it can make its own decisions, and user control mode where a user can control it remotely. It is designed using a microcontroller and can perform tasks like object recognition using computer vision and color detection in MATLAB, as well as wall painting using pneumatic systems. The robot's movement is controlled by DC motors and it uses sensors like ultrasonic sensors and gas sensors to navigate autonomously. RF transmission allows communication between the robot and a remote control unit. The overall aim is to develop a low-cost robotic system for industrial applications like material handling.
This document reviews cryptography techniques to secure the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad-hoc networks. It discusses various types of attacks on AODV like impersonation, denial of service, eavesdropping, black hole attacks, wormhole attacks, and Sybil attacks. It then proposes using the RC6 cryptography algorithm to secure AODV by encrypting data packets and detecting and removing malicious nodes launching black hole attacks. Simulation results show that after applying RC6, the packet delivery ratio and throughput of AODV increase while delay decreases, improving the security and performance of the network under attack.
The document describes a proposed modification to the conventional Booth multiplier that aims to increase its speed by applying concepts from Vedic mathematics. Specifically, it utilizes the Urdhva Tiryakbhyam formula to generate all partial products concurrently rather than sequentially. The proposed 8x8 bit multiplier was coded in VHDL, simulated, and found to have a path delay 44.35% lower than a conventional Booth multiplier, demonstrating its potential for higher speed.
This document discusses image deblurring techniques. It begins by introducing image restoration and focusing on image deblurring. It then discusses challenges with image deblurring being an ill-posed problem. It reviews existing approaches to screen image deconvolution including estimating point spread functions and iteratively estimating blur kernels and sharp images. The document also discusses handling spatially variant blur and summarizes the relationship between the proposed method and previous work for different blur types. It proposes using color filters in the aperture to exploit parallax cues for segmentation and blur estimation. Finally, it proposes moving the image sensor circularly during exposure to prevent high frequency attenuation from motion blur.
This document describes modeling an adaptive controller for an aircraft roll control system using PID, fuzzy-PID, and genetic algorithm. It begins by introducing the aircraft roll control system and motivation for developing an adaptive controller to minimize errors from noisy analog sensor signals. It then provides the mathematical model of aircraft roll dynamics and describes modeling the real-time flight control system in MATLAB/Simulink. The document evaluates PID, fuzzy-PID, and PID-GA (genetic algorithm) controllers for aircraft roll control and finds that the PID-GA controller delivers the best performance.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Lsb Based Digital Image Watermarking For Gray Scale Image
1. IOSR Journal of Computer Engineering (IOSRJCE)
ISSN: 2278-0661, ISBN: 2278-8727 Volume 6, Issue 1 (Sep-Oct. 2012), PP 36-41
www.iosrjournals.org
www.iosrjournals.org 36 | Page
Lsb Based Digital Image Watermarking For Gray Scale Image
Deepshikha Chopra1
, Preeti Gupta2
, Gaur Sanjay B.C.3
, Anil Gupta4
1,2
(CSE Department, Jodhpur Institute of Engineering & Technology Jodhpur, RTU, India)
3
(ECE Department, Jodhpur Institute of Engineering & Technology Jodhpur, RTU, India)
4
(CSE Department, M.B.M. Engineering College, JNVU Jodhpur, India)
ABSTRACT : In recent years, internet revolution resulted in an explosive growth in multimedia applications.
The rapid advancement of internet has made it easier to send the data/image accurate and faster to the
destination. Besides this, it is easier to modify and misuse the valuable information through hacking at the same
time. Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A
watermark is a form, image or text that is impressed onto paper, which provides evidence of its authenticity.
In this paper an invisible watermarking technique (least significant bit) and a visible watermarking technique is
implemented.
This paper presents the general overview of image watermarking and different security issues. Various
attacks are also performed on watermarked images and their impact on quality of images is also studied. In this
paper, Image Watermarking using Least Significant Bit (LSB) algorithm has been used for embedding the
message/logo into the image. This work has been implemented through MATLAB.
Keywords - Watermarking, Least Significant Bit (LSB), JPEG (Joint Photographic Experts Group), Mean
Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).
I. INTRODUCTION
Watermarking is a technique used to hide data or identifying information within digital multimedia.
Our discussion will focus primarily on the watermarking of digital images, though digital video, audio, and
documents are also routinely watermarked. Digital watermarking is becoming popular, especially for adding
undetectable identifying marks, such as author or copyright information.
The digital watermarking process embeds a signal into the media without significantly degrading its
visual quality. Digital watermarking is a process to embed some information called watermark into different
kinds of media called Cover Work [1, 3]. 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. Digital watermarking involves embedding a structure in a
host signal to “mark” its ownership [4]. Digital watermarks are inside the information so that ownership of the
information cannot be claimed by third party [5]. While some watermarks are visible [6], most watermarks are
invisible.
The best known Watermarking method that works in the Spatial Domain is the Least Significant Bit
(LSB), which replaces the least significant bits of pixels selected to hide the information. This method has
several implementation versions that improve the algorithm in certain aspects.
II. BASIC MODEL
Basic model of watermarking with four stages as shown in figure 2.1 below: [5, 8]
Generation
Embedding
Distribution and attacks
Detection and Recovery.
Explanation about the Generation, Embedding and Detection stage is presented together.
Figure 2.1 Basic Model
Generation Embedding Distribution
and Attacks
Detection
and
Recovery
2. LSB Based Image Watermarking For Gray Scale Image
www.iosrjournals.org 37 | Page
In Generation stage watermark is created and its contents should be unique and complex such that it is difficult
to extract or damaged from possible attacks.
In Embedding stage watermark is embedded in cover media. Embedding is directly related to extraction
algorithm. Hence Embedding algorithm is combination of watermark with chosen media, so the result is
equivalently:
WM= E (CI, WI)
Where CI is original image, WI is watermark, E is embedding function and WM is the watermarked media.
The Distribution process can be seen as transmission of signal through watermark channel. Possible attacks in
broadcast channel may be intentional or accidental.
Detection process allows owner to be identified and provides information to the intended recipients.
There are two kinds of detection: Informed detection and Blind detection.
To insert a watermark we can use spatial domain, transform domain.
2.1 CLASSIFICATION OF WATERMARKING
Digital Watermarking techniques can be classified as:
Text Watermarking
Image Watermarking
Audio Watermarking
Video Watermarking
In other way, the digital watermarks can be divided into three different types as follows:
i. Visible watermark
ii. Invisible-Robust watermark
iii. Invisible-Fragile watermark
2.2 CLASSIFICATION OF WATERMARKING ATTACKS
Many operations may affect the watermarking algorithms and destroy it. Those operations that destroy
watermark data are called attacks [7]. Here are some of the best known attacks.
Simple attacks: (other possible names include “waveform attacks” and “noise attacks”) are
conceptually simple attacks that attempt to impair the embedded watermark by manipulations of the
whole watermarked data (host data plus watermark) without an attempt to identify and isolate the
watermark.
Detection-disabling attacks: (other possible names include “synchronization attacks”) are attacks that
attempt to break the correlation and to make the recovery of the watermark impossible or infeasible for a
watermark detector, mostly by geometric distortion like zooming, shift in (for video) direction, rotation,
cropping, pixel permutations, subsampling, removal or insertion of pixels or pixel clusters, or any other
geometric transformation of the data.
Ambiguity attacks: (other possible names include “deadlock attacks,” “inversion attacks,” “fake
watermark attacks,” and “fake-original attacks”) are attacks that attempt to confuse by producing fake
original data or fake watermarked data.
Removal attacks: are attacks that attempt to analyze the watermarked data, estimate the watermark or
the host data, separate the watermarked data into host data and watermark and discard only the
watermark.
Cryptographic attacks: The above two type of attacks, removal and geometric, do not breach the
security of the watermarking algorithm. On the other hand, cryptographic attacks deal with the cracking
of the security.
III. SPATIAL DOMAIN TECHNIQUES
Techniques in spatial domain class generally share the following characteristics:
The watermark is applied in the pixel domain.
No transforms are applied to the host signal during watermark embedding.
Combination with the host signal is based on simple operations, in the pixel domain.
The watermark can be detected by correlating the expected pattern with the received signal.
3.1 REVIEW OF LSB
In a digital image, information can be inserted directly into every bit of image information or the more busy
areas of an image can be calculated so as to hide such messages in less perceptible parts of an image [4],[7].
Tirkel et. al were one of the first used techniques for image watermarking. Two techniques were presented to
3. LSB Based Image Watermarking For Gray Scale Image
www.iosrjournals.org 38 | Page
hide data in the spatial domain of images by them. These methods were based on the pixel value‟s Least
Significant Bit (LSB) modifications.
The algorithm proposed by Kurah and McHughes [9] to embed in the LSB and it was known as image
downgrading [2]. An example of the less predictable or less perceptible is Least Significant Bit insertion. This
section explains how this works for an 8-bit grayscale image and the possible effects of altering such an image.
The principle of embedding is fairly simple and effective. If we use a grayscale bitmap image, which is 8- bit,
we would need to read in the file and then add data to the least significant bits of each pixel, in every 8-bit pixel.
In a grayscale image each pixel is represented by 1 byte consist of 8 bits. It can represent 256 gray colors
between the black which is 0 to the white which is 255.
The principle of encoding uses the Least Significant Bit of each of these bytes, the bit on the far right side. If
data is encoded to only the last two significant bits (which are the first and second LSB) of each color
component it is most likely not going to be detectable; the human retina becomes the limiting factor in viewing
pictures [7].
For the sake of this example only the least significant bit of each pixel will be used for embedding
information. If the pixel value is 138 which is the value 10000110 in binary and the watermark bit is 1, the value
of the pixel will be 10000111 in binary which is 139 in decimal. In this example we change the underline pixel.
3.2 WATERMARK EMBEDDED AND EXTRACTION
All images are 256*256 Pixels by 8 bit per pixel gray scale image.
Select an image CI to be used as base image or cover image in which watermark will be inserted. Select an
image to be used as watermark Reading images WI which will be added to base image.
n: integer
n=no. of least significant bits to be utilized to hide most significant bits of watermark under the base
image
Figure 3.1 Watermark Embedded Figure 3.2 Watermark Extraction
Watermark Embedded
For each pixel in base, watermark, watermarked_image
Do
Base_image:set n least significant bits to zero
Watermark:shift right by 8-n bits
Watermarked-image : add values from base and watermark
Enddo
End
Watermark Extraction
In watermarked image for each pixel in watermarked image and extracted image
Do
Watermarked image:
Shift left by 8-n bits
Extracted image:
Set to the shifted value of watermarked image
The technique used will be LSB technique which is a form of spatial domain technique.
This technique is used to add an invisible and visible watermark in the image by varying the number of bits
to be replaced in base image.
IV. EXPERIMENTAL RESULTS
The Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR) are the two error metrics used to
compare image compression quality. This ratio is often used as a quality measurement between the original and
Cover Image (CI) Watermark Image (WI)
Watermarked Embedding Algorithm
Watermarked Image (WM)
Cover Image (CI) Watermark Image (WI)
Watermarked Extraction Algorithm
Watermarked Image (WM)
4. LSB Based Image Watermarking For Gray Scale Image
www.iosrjournals.org 39 | Page
a watermarked image. If one of the signals is an original signal of acceptable (or perhaps pristine) quality, and
the other is a distorted version of it whose quality is being evaluated, then the MSE may also be regarded as a
measure of signal quality.
MSE is a signal fidelity measure. The goal of a signal fidelity measure is to compare two signals by
providing a quantitative score that describes the level of error/distortion between them. Usually, it is assumed
that one of the signals is a pristine original, while the other is distorted or contaminated by errors.
Suppose that x = { xi |i = 1, 2, · · ·, N} and y = { yi |i = 1, 2, · · · , N} are two finite-length, discrete
signals (e.g., visual images), where N is the number of signal samples (pixels, if the signals are images) and xi
and yi are the values of the i th samples in x and y, respectively. The MSE between the signals x and y is
2
1
1
( , ) ( )i i
N
i
MSE x y
N
x y
In the MSE, we will often refer to the error signal ei,= xi − yi, which is the difference between the original and
distorted signals. If one of the signals is an original signal of acceptable (or perhaps pristine) quality, and the
other is a distorted version of it whose quality is being evaluated, then the MSE may also be regarded as a
measure of signal quality.
MSE is often converted into a peak-to-peak signal-to-noise ratio (PSNR) measure
10
2
10log
L
PSNR
MSE
Where L is the dynamic range of allowable image pixel intensities. For example, for images that have
allocations of 8 bits/pixel of gray-scale, L = 28
− 1 = 255. The PSNR is useful if images having different
dynamic ranges are being compared, but otherwise contains no new information relative to the MSE.
Cover Image CI (Flower) Watermark Image WI (logo)
1st
bit substitution 2nd
bit substitution 3rd
bit substitution 4th
bit substitution
(Invisible Watermarked Image)
5th
bit substitution 6th
bit substitution 7th bit substitution 8th
bit substitution
(Visible Watermarked Image)
The figure 4.1 shows various images, WI, upon which the algorithm was implemented and their corresponding
watermarked copy WM. Values for mean square error (MSE) and peak signal to noise ratio (PSNR) are
measured. Table 4.1
5. LSB Based Image Watermarking For Gray Scale Image
www.iosrjournals.org 40 | Page
Method PSNR MSE
LSB or 1st Bit Substitution 54.87 0.21
2nd Bit Substitution 45.54 1.83
3rd Bit Substitution 38.25 9.80
4th Bit Substitution 31.68 44.50
5th Bit Substitution 25.42 188.28
6th Bit Substitution 19.28 772.72
7th Bit Substitution 13.21 3129.01
MSB or 8th Bit Substitution 14.3467 2.3900e+003
TABLE 4.1 PSNR & MSE for Different Bit Substitution
V. IMAGES WITH DISTORTIONS
Here we have applied different types of distortion to the watermarked image and the Mean Squared
Error (MSE) for the images is calculated. The traditional error measuring techniques are mainly MSE and Peak
Signal to Noise Ratio (PSNR). These are widely used because they are simple to calculate and are independent
of viewing conditions and individual observers.
VARIOUS ATTACKS ON THE WATERMARKED IMAGE
1. Salt and Paper Noise
Bit
Substitution
Watermarked Image Salt and Paper Noise
PSNR MSE PSNR MSE
Bit=1 141.40 142.25 26.63 140.33
Bit=2 143.97 142.11 26.64 137.59
Bit=3 148.73 139.83 26.71 134.84
Bit=4 145.69 136.52 26.53 144.24
Bit=5 143.86 136.65 26.81 149.50
Bit=6 131.87 132.88 26.93 135.00
Bit=7 136.97 133.00 26.93 138.74
Bit=8 184.13 187.42 25.44 180.95
2. Gaussian Noise
Bit
Substitution
Watermarked Image Gaussian Noise
PSNR MSE PSNR MSE
Bit=1 205.67 203.96 25.07 204.85
Bit=2 206.11 205.22 25.04 203.76
Bit=3 204.74 204.71 25.05 207.45
Bit=4 205.57 205.86 25.03 207.47
Bit=5 205.40 205.40 25.04 207.62
Bit=6 204.45 207.13 25.00 207.14
Bit=7 197.64 198.67 25.18 199.47
Bit=8 193.72 190.94 25.36 194.93
3. Poisson Noise
Bit
Substitution
Watermarked Image Poisson Noise
PSNR MSE PSNR MSE
Bit=1 34.06 34.57 32.78 33.70
Bit=2 34.27 34.09 32.84 34.74
Bit=3 34.62 34.47 32.79 34.81
Bit=4 34.81 34.89 32.74 34.51
Bit=5 35.78 35.23 32.70 35.39
Bit=6 33.64 33.92 32.86 33.65
Bit=7 37.52 38.00 32.37 38.23
Bit=8 36.24 35.96 32.61 36.28
6. LSB Based Image Watermarking For Gray Scale Image
www.iosrjournals.org 41 | Page
4. Speckle Noise
Bit
Substitution
Watermarked Image Speckle Noise
PSNR MSE PSNR MSE
Bit=1 49.75 49.57 31.21 49.26
Bit=2 49.45 49.83 31.19 49.56
Bit=3 49.70 50.33 31.15 50.11
Bit=4 50.51 50.31 31.15 49.82
Bit=5 50.84 51.13 31.08 51.10
Bit=6 48.52 48.78 31.28 48.75
Bit=7 56.21 55 62 30.71 56.15
Bit=8 75.30 74.26 29.46 74.85
Noise on ‘Bit=1’
Speckle Noise Poisson Noise Gaussian Noise Salt and Paper Noise
VI CONCLUSION
The increasing amount of digital exchangeable data generates new information security needs.
Multimedia documents and specifically images are also affected. Users expect that robust solutions will ensure
copyright protection and also guarantee the authenticity of multimedia documents. In the current state of
research, it is difficult to affirm which watermarking approach seems most suitable to ensure an integrity service
adapted to images and more general way to multimedia documents.
The tool used for the execution of this algorithm was „Matlab‟. The aim of the program is to replace the
LSB of the base image with the MSB of the watermark.
REFERENCES
Journal Papers:
[1] Preeti Gupta, “Cryptography based digital image watermarking algorithm to increase security of watermark data”, International
Journal of Scientific & Engineering Research, Volume 3, Issue 9 (September 2012) ISSN 2229-5518
[2] Manpreet Kaur, Sonika Jindal, Sunny Behal, “A Study of Digital Image Watermarking”, IJREAS ,Volume 2, Issue 2 (Febru ry
2012) pp-126-136
[3] B Surekha, Dr GN Swamy, “A Spatial Domain Public Image Watermarking”, International Journal of Security and Its
Applications Vol. 5 No. 1, January, 2011
[4] Robert, L., and T. Shanmugapriya, “A Study on Digital Watermarking Techniques ”, International Journal of Recent Trends in
Engineering, vol. 1, no. 2, pp. 223-225, 2009.
[5] H.Arafat Ali, “Qualitative Spatial Image Data Hiding for Secure Data Transmission”, GVIP Journal,Volume 7,Issue 2 , pages 35-
37, 2, August 2007
[6] Cox, Miller and Bloom, “Digital watermarking”, 1st edition 2001, San Fransisco: Morgan Kaufmann Publisher
[7] Brigitte Jellinek, “Invisible Watermarking of Digital Images for Copyright Protection” University Salzburg, pp. 9 – 17, Jan 2000.
[8] Lu, C-S., Liao, H-Y., M., Huang, S-K., Sze, C-J., “Cocktail Watermarking on Images”, 3rd International Workshop on Information
Hiding, Dresden, Germany, Sep 29-Oct. 1, 1999
[9] Dr. Martin Kutter and Dr. Frederic Jordan, “Digital Watermarking Technology”, AlpVision, Switzerland, pp 1 – 4M Ozaki, Y.
Adachi, Y. Iwahori, and N. Ishii, Application of fuzzy theory to writer recognition of Chinese characters, International Journal of
Modelling and Simulation, 18(2), 1998, 112-116.
[10] J.J.K.O. Ruanaidh, W.J.Dowling, F.M. Boland, “Watermarking Digital Images for Copyright Protection”, IEEE ProcVis. Image
Signal Process. Vol. 143, No. 4, pp 250 - 254. August 1996.
Thesis:
[11] Mitra Abbasfard “Digital Image Watermarking Robustness: A Comparative Study”, Delft University of Technologyrertre , 2009:
74 pages
[12] Harpuneet Kaur , “Robust Image Watermarking Technique to Increase Security and Capacity of Watermark Data” Thapar
Institute of Engineering & Technology , Patiala , India ,May 2006 : 79 pages
[13] K. Vanwasi, “Digital Watermarking - Steering the future of security” Edition 2001, available at
http://www.networkmagazineindia.com/200108/security1.html
[14] Saraju Prasad Mohanty, “Watermarking of Digital Images”, Indian Institute of Science Bangalore, pp. 1.3 – 1.6, January 1999
Books:
[15] Andreas Koschan, Mongi Abidi, “ Digital Colour Image Processing” Published by John Wiley & Sons, Inc., Hoboken, New
Jersey,Published simultaneously in Canada.