Linear Hash Functions as a Means of Distortion--Rate Optimization in Data Embedding
Pages 235 - 238
Abstract
Embedding hidden data is usually performed by introducing some distortions (errors) in cover objects. If the distortions exceed a certain bound, steganalysis can detect the presence of hidden data. So the problem is to embed as much data as possible and not exceed a permissible distortion level to ensure indetectability. We describe a general class of stegosystems that solves the problem by employing linear hash functions. The suggested stegosystems allow to transmit hidden information of the amount asymptotically close to the maximum possible under the given distortion.
References
[1]
Tomávs Filler, Jan Judas, and Jessica Fridrich. 2011. Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans. on Info. Forensics and Security6, 1, 920--935.
[2]
Vojtvech Holub, Jessica Fridrich, and Tomávs Denemark. 2014.Universal distortion function for steganography in an arbitrary domain. EURASIP Journal on Information Security 2014, 1.
[3]
Christian Cachin. 2004. An information-theoretic model of steganography. Information and Computation192, 1 (July 2004), 41--56.
[4]
Boris Ryabko and Daniil Ryabko. 2011.Constructing perfect steganographic systems Information and Computation 209, 9 (September 2011), 1223--1230.
[5]
Thanh Hai Thai, Rémy Cogranne, and Florent Retraint. 2014.Statistical model of quantized DCT coefficients: application in the steganalysis of Jsteg algorithm. IEEE Trans. Image Processing 23, 5 (May 2014), 1980--1993.
[6]
Vahid Sedighi, Rémy Cogranne, and Jessica Fridrich. 2016.Content-adaptive steganography by minimizing statistical detectability. IEEE Trans. Inform. Forensics and Security 11, 2, 221--234.
[7]
Thomas M. Cover and Joy A. Thomas. 2006. Elements of Information Theory (2nd. ed.).Wiley-Interscience, New York, NY.
[8]
[9]
Ron Crandall. 1998. Some notes on steganography. (December 1998).Retrieved February 2, 2019 fromhttp://dde.binghamton.edu/download/Crandall_matrix.pdf
[10]
[11]
Andreas Westfeld. 2001.High capacity despite better steganalysis (F5--A steganographic algorithm).In Proceedings of the 4th International Workshop on Information Hiding. LNCS 2137, Springer-Verlag, Berlin, Heidelberg, 289--302.
[12]
[13]
F. Galand and Grigory Kabatiansky. 2003.Information hiding by coverings. In Proceedings 2003 IEEE Information Theory Workshop. 151--154.
[14]
[15]
J”urgen Bierbrauer and Jessica Fridrich. 2008.Constructing good covering codes for applications in steganography. Transactions on Data Hiding and Multimedia Security III. LNCS 4920, Springer, Berlin, Heidelberg, 1--22.
[16]
[17]
Alfred J. Menezes, Paul C. van Oorschot, and Scott A. Vanstone. 1996. Handbook of Applied Cryptography. CRC Press.
[18]
[19]
Jessica Fridrich and Miroslav Goljan. 2000.Robust hash functions for digital watermarking. In Proceedings of International Conference on Information Technology: Coding and Computing. IEEE, 173--178.
[20]
[21]
Valentin F. Kolchin, Boris A. Sevastyanov, and Vladimir P. Chistyakov. 1978. Random Allocations. V. H. Winston, Washington, New York, distributed solely by Halsted Press.
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Information & Contributors
Information
Published In
July 2019
249 pages
ISBN:9781450368216
DOI:10.1145/3335203
- General Chairs:
- Rémi Cogranne,
- Luisa Verdoliva,
- Program Chairs:
- Siwei Lyu,
- Juan Pastoriza,
- Xinpeng Zhang
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Publication History
Published: 02 July 2019
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- Short-paper
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- The Russian Foundation for Basic Research
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IH&MMSec '19
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