IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Detection of LSB Steganography via Sample Pair Analysis
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Defending against statistical steganalysis
SSYM'01 Proceedings of the 10th conference on USENIX Security Symposium - Volume 10
IH'04 Proceedings of the 6th international conference on Information Hiding
On the limits of steganography
IEEE Journal on Selected Areas in Communications
Review: A review on blind detection for image steganography
Signal Processing
On completeness of feature spaces in blind steganalysis
Proceedings of the 10th ACM workshop on Multimedia and security
Novelty detection in blind steganalysis
Proceedings of the 10th ACM workshop on Multimedia and security
Benchmarking for Steganography
Information Hiding
Multi-class Blind Steganalysis Based on Image Run-Length Analysis
IWDW '09 Proceedings of the 8th International Workshop on Digital Watermarking
Multi-party covert communication with steganography and quantum secret sharing
Journal of Systems and Software
Vision of the unseen: Current trends and challenges in digital image and video forensics
ACM Computing Surveys (CSUR)
A feature selection methodology for steganalysis
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Steganalysis based on differential statistics
CANS'06 Proceedings of the 5th international conference on Cryptology and Network Security
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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In this paper, we use the previously proposed calibrated DCT features [9] to construct a Support Vector Machine classifier for JPEG images capable of recognizing which steganographic algorithm was used for embedding. This work also constitutes a more detailed evaluation of the performance of DCT features as in [9] only a linear classifier was used. The DCT features transformed using Principal Component Analysis enable an interesting visualization of different stego programs in a three-dimensional space. This paper demonstrates that, at least under some simplifying assumptions in which the effects of double compression are ignored, it is possible to reliably classify stego images to their embedding techniques. The classifier is capable of generalizing to previously unseen techniques.