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Steganography

About: Steganography is a research topic. Over the lifetime, 11214 publications have been published within this topic receiving 161523 citations.


Papers
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Journal ArticleDOI
TL;DR: This work explores both traditional and novel techniques for addressing the data-hiding process and evaluates these techniques in light of three applications: copyright protection, tamper-proofing, and augmentation data embedding.
Abstract: Data hiding, a form of steganography, embeds data into digital media for the purpose of identification, annotation, and copyright. Several constraints affect this process: the quantity of data to be hidden, the need for invariance of these data under conditions where a "host" signal is subject to distortions, e.g., lossy compression, and the degree to which the data must be immune to interception, modification, or removal by a third party. We explore both traditional and novel techniques for addressing the data-hiding process and evaluate these techniques in light of three applications: copyright protection, tamper-proofing, and augmentation data embedding.

3,037 citations

Book
23 Nov 2007
TL;DR: 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 applications

1,773 citations

Book
31 Dec 1999
TL;DR: This first comprehensive survey of steganography and watermarking and their application to modern communications and multimedia and an overview of "steganalysis," methods which can be used to break steganographic communication are discussed.
Abstract: From the Publisher: Steganography, a means by which two or more parties may communicate using "invisible" or "subliminal" communication, and watermarking, a means of hiding copyright data in images, are becoming necessary components of commercial multimedia applications that are subject to illegal use. This new book is the first comprehensive survey of steganography and watermarking and their application to modern communications and multimedia. Handbook of Information Hiding: Steganography and Watermarking helps you understand steganography, the history of this previously neglected element of cryptography, the hurdles of international law on strong cryptographic techniques, a description of possible applications, and a survey of the methods you can use to hide information in modern media. Included in this discussion is an overview of "steganalysis," methods which can be used to break steganographic communication. This comprehensive resource also includes an introduction to and survey of watermarking methods, and discusses this method's similarities and differences to steganography. You gain a working knowledge of watermarking's pros and cons, and you learn the legal implications of watermarking and copyright issues on the Internet.

1,732 citations

Journal ArticleDOI
TL;DR: This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature and some recommendations and advocates for the object-oriented embedding mechanism.

1,572 citations

Journal ArticleDOI
TL;DR: A novel general strategy for building steganography detectors for digital images by assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters.
Abstract: We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo , and optimally coded ternary ±1 embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of steganographic schemes.

1,553 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20242
2023481
20221,067
2021661
2020817
2019905