Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Attacks on Steganographic Systems
IH '99 Proceedings of the Third International Workshop on Information Hiding
Hide and Seek: An Introduction to Steganography
IEEE Security and Privacy
An information-theoretic model for steganography
Information and Computation
Statistically undetectable jpeg steganography: dead ends challenges, and opportunities
Proceedings of the 9th workshop on Multimedia & security
A general framework for structural steganalysis of LSB replacement
IH'05 Proceedings of the 7th international conference on Information Hiding
Secret key estimation in sequential steganography
IEEE Transactions on Signal Processing
Detection of LSB steganography via sample pair analysis
IEEE Transactions on Signal Processing
Steganalysis using higher-order image statistics
IEEE Transactions on Information Forensics and Security
Steganalysis for Markov cover data with applications to images
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security
Security and Robustness Enhancement for Image Data Hiding
IEEE Transactions on Multimedia
Rate-distortion theory for the Shannon cipher system
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
On joint coding for watermarking and encryption
IEEE Transactions on Information Theory
Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions
IEEE Transactions on Information Theory
Steganalysis using image quality metrics
IEEE Transactions on Image Processing
Joint security and robustness enhancement for quantization based data embedding
IEEE Transactions on Circuits and Systems for Video Technology
Steganographic manipulations with elliptic curve cryptography
International Journal of Electronic Security and Digital Forensics
Multi-annulus partition based image representation for image classification
International Journal of Sensor Networks
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In this paper, we propose a computationally-efficient data hiding method which achieves Cachin's security criterion: zero Kullback-Liebler (KL) divergence. To preserve statistical properties of the cover medium, we swap pixels rather than modify them to hide information. We theoretically analyse the security of the proposed method from various perspectives. Upper bounds of the KL divergence of second order statistics; The relationship between distortions in the DCT domain and embedding positions in the spatial domain; The upper bound on the conditional entropy in the DCT domain. We then subject our proposed stego method to several practical steganalysis algorithms: Histogram based attacks; A higher-order statistics based universal steganalysis algorithm; A new learning based steganalysis that specifically for this hiding algorithm. Experimental results show that our data hiding method can prevent these statistical detection methods, when the embedding rate is less than or equal to 10%.