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Steganography in Digital Media: Principles, Algorithms, and Applications
Abstract
Steganography, the art of hiding of information in apparently innocuous objects or images, is a field with a rich heritage, and an area of rapid current development. This clear, self-contained guide shows you how to understand the building blocks of covert communication in digital media files and how to apply the techniques in practice, including those of steganalysis, the detection of steganography. Assuming only a basic knowledge in calculus and statistics, the book blends the various strands of steganography, including information theory, coding, signal estimation and detection, and statistical signal processing. Experiments on real media files demonstrate the performance of the techniques in real life, and most techniques are supplied with pseudo-code, making it easy to implement the algorithms. The book is ideal for students taking courses on steganography and information hiding, and is also a useful reference for engineers and practitioners working in media security and information assurance.
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Citations
Cited by
Rich Models for Steganalysis of Digital Images
Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144
Self-citationLimits of Reliable Communication with Low Probability of Detection on AWGN Channels
Bash, B. A., Goeckel, D., & Towsley, D. (2013) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2013.130923 · Cited by 1,075
Reversible data hiding: Advances in the past two decades
Shi, Y.-Q., Li, X., Zhang, X., Wu, H.-T., & Ma, B. (2016) · IEEE Access · doi:10.1109/access.2016.2573308 · Cited by 594
RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks
Yang, Z.-L., Guo, X.-Q., Chen, Z.-M., Huang, Y.-F., & Zhang, Y.-J. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2871746 · Cited by 322
Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
Yu, N., Skripniuk, V., Abdelnabi, S., & Fritz, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01418 · Cited by 195
Metadata
- Authors
- Fridrich, J.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2009-11-12
- OpenAlex
- W4253000688
- Cited by
- 570
- References
- 0
- FWCI
- 2.89
- Type
- book
- Language
- en
- Open access
- closed
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