Optimal closed boundary identification in gray-scale imagery
Journal of Mathematical Imaging and Vision
Image analysis with partially ordered Markov models
Computational Statistics & Data Analysis
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Defending against statistical steganalysis
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
Proceedings of the 11th ACM workshop on Multimedia and security
Steganalysis by subtractive pixel adjacency matrix
Proceedings of the 11th ACM workshop on Multimedia and security
Steganography in Digital Media: Principles, Algorithms, and Applications
Steganography in Digital Media: Principles, Algorithms, and Applications
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
IH'04 Proceedings of the 6th international conference on Information Hiding
Steganalysis for Markov cover data with applications to images
IEEE Transactions on Information Forensics and Security
Benchmarking for steganography by kernel fisher discriminant criterion
Inscrypt'11 Proceedings of the 7th international conference on Information Security and Cryptology
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The field of steganalysis has blossomed prolifically in the past few years, providing the community with a number of very good blind steganalyzers. Features for blind steganalysis are generated in many different ways, typically using statistical measures. This paper presents a new image modeling technique for steganalysis that uses as features the conditional probabilities described by a stochastic model called a partially ordered Markov model (POMM). The POMM allows concise modeling of pixel dependencies among quantized discrete cosine transform coefficients. We develop a steganalyzer based on support vector machines that distinguishes between cover and stego JPEG images using 98 POMM features. We show that the proposed steganalyzer outperforms two comparative Markov-based steganalyzers [25,6] and outperforms a third steganalyzer [23] on half of the tested classes, by testing our approach with many different image databases on five embedding algorithms, with a total of 20,000 images.