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Digital Watermarking and Steganography
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TLDR
This new edition now contains essential information on steganalysis and steganography, and digital watermark embedding is given a complete update with new processes and applications.Abstract:
Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.
*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applicationsread more
Citations
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Proceedings ArticleDOI
Reliable deniable communication: Hiding messages in noise
TL;DR: In this paper, it was shown that Alice can reliably send a message to Bob over a binary symmetric channel (BSC) while ensuring that her transmission is deniable from an eavesdropper Willie.
Journal ArticleDOI
Reversible data hiding exploiting spatial correlation between sub-sampled images
TL;DR: Experimental results support that the proposed reversible data hiding method provides high embedding capacity while keeping the distortions at a low level.
Proceedings ArticleDOI
Color image database for evaluation of image quality metrics
Nikolay N. Ponomarenko,Vladimir V. Lukin,Karen Egiazarian,Jaakko Astola,Marco Carli,Federica Battisti +5 more
TL;DR: A new image database for testing full-reference image quality assessment metrics is presented, based on 1700 test images, which can be used for evaluating the performances of visual quality metrics as well as for comparison and for the design of new metrics.
Proceedings ArticleDOI
Embedding Watermarks into Deep Neural Networks
TL;DR: In this paper, the authors propose to use a digital watermarking technology to protect intellectual property or detect intellectual property infringement of trained models, and they also define requirements, embedding situations, and attack types for water-marking to deep neural networks.
Journal ArticleDOI
Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework
TL;DR: A generic hybrid deep-learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models is proposed, and it is demonstrated that the framework is insensitive to JPEG blocking artifact alterations, and the learned model can be easily transferred to a different attacking target and even a different data set.
References
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