Universal Detection of JPEG Steganography

In this paper, we present a novel universal ap- proach which consists in exploring statistics in the com- pressed frequency domain. This approach is motivated by two main characteristics of the lossless compression step of the JPEG format. First, this step can be considered as a bijective mapping and then, when only few bits are flipped at its input, half the bits are flipped at the output. These properties, combined with a binary entropy deviation we pointed out, enable the design of detection schemes which the efficiencies are constant and do not depend in practice on the amount of information that has been embedded. These characteristics define a new class of promising functions for steganalysis. We illustrate our technique by considering RLE plus Huffman as such a function and design a new efficient universal steganalytic scheme to blindly detect the use of Outguess, F5 and JPHide and JPseek. Experimental results show that our steganalysis scheme is able to efficiently detect the use of new algorithms which are not used during the training step, even if the embedding rate is very low (� 10 6 ). As expected, the accuracy of our detector is independent of the payload.

[1]  Jessica J. Fridrich,et al.  New methodology for breaking steganographic techniques for JPEGs , 2003, IS&T/SPIE Electronic Imaging.

[2]  Jessica J. Fridrich,et al.  Feature-Based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes , 2004, Information Hiding.

[3]  Siwei Lyu,et al.  Steganalysis using higher-order image statistics , 2006, IEEE Transactions on Information Forensics and Security.

[4]  Eric Filiol,et al.  New Features for Specific JPEG Steganalysis , 2008 .

[5]  Jessica J. Fridrich,et al.  Steganalysis of JPEG Images: Breaking the F5 Algorithm , 2002, Information Hiding.

[6]  Andreas Westfeld,et al.  F5-A Steganographic Algorithm , 2001, Information Hiding.

[7]  Nasir D. Memon,et al.  Image Steganography and Steganalysis: Concepts and Practice , 2003, IWDW.

[8]  William A. Pearlman,et al.  Kernel Fisher discriminant for steganalysis of JPEG hiding methods , 2004, IS&T/SPIE Electronic Imaging.

[9]  Nasir D. Memon,et al.  Steganalysis based on image quality metrics , 2001, 2001 IEEE Fourth Workshop on Multimedia Signal Processing (Cat. No.01TH8564).

[10]  Siwei Lyu,et al.  Steganalysis using color wavelet statistics and one-class support vector machines , 2004, IS&T/SPIE Electronic Imaging.

[11]  Eric Filiol,et al.  Universal JPEG Steganalysis in the Compressed Frequency Domain , 2006, IWDW.

[12]  C. Wayne Brown.  Graphics File Formats , 1994 .

[13]  Gustavus J. Simmons,et al.  The Prisoners' Problem and the Subliminal Channel , 1983, CRYPTO.

[14]  Andreas Pfitzmann,et al.  Attacks on Steganographic Systems , 1999, Information Hiding.

[15]  Hany Farid,et al.  Detecting hidden messages using higher-order statistical models , 2002, Proceedings. International Conference on Image Processing.

[16]  Barry J. Shepherd,et al.  Graphics file formats - reference and guide , 1994 .

[17]  Niels Provos,et al.  Defending Against Statistical Steganalysis , 2001, USENIX Security Symposium.

[18]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[19]  Niels Provos,et al.  Hide and Seek: An Introduction to Steganography , 2003, IEEE Secur. Priv..

[20]  Tomás Pevný,et al.  Multi-class blind steganalysis for JPEG images , 2006, Electronic Imaging.

[21]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[22]  H. Feistel.  Cryptography and Computer Privacy , 1973 .

[23]  Siwei Lyu,et al.  Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines , 2002, Information Hiding.

[24]  Chia-Hung Yeh,et al.  Data hiding domain classification for blind image steganalysis , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).