Steganalysis of multi-class JPEG images based on expanded Markov features and polynomial fitting

In this article, based on the Markov approach proposed by shi et al., we expand it to the inter-blocks of the DCT domain, calculate the difference of the expanded Markov features between the testing image and the calibrated version, and combine these difference features and the polynomial fitting features on the histogram of the DCT coefficients as detectors. We reasonably improve the detection performance in multi-class JPEG images. We also compare the steganalysis performance among the feature reduction/selection methods based on principal component analysis, singular value decomposition, and Fisherpsilas linear discriminant.

[1]  O. Roeva,et al.  Information Hiding: Techniques for Steganography and Digital Watermarking , 2000 .

[2]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[3]  Andrew D. Ker.  Improved Detection of LSB Steganography in Grayscale Images , 2004, Information Hiding.

[4]  Petra Mutzel,et al.  A Graph-Theoretic Approach to Steganography , 2005, Communications and Multimedia Security.

[5]  T. Pham,et al.  Analysis of Microarray Gene Expression Data , 2006 .

[6]  Qingzhong Liu,et al.  Feature Mining and Neuro-Fuzzy Inference System for Steganalysis of LSB Matching Stegangoraphy in Grayscale Images , 2007, IJCAI.

[7]  Qingzhong Liu,et al.  Image Complexity and Feature Extraction for Steganalysis of LSB Matching Steganography , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[8]  Jonny Eriksson,et al.  Feature reduction for classification of multidimensional data , 2000, Pattern Recognit..

[9]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[10]  Qingzhong Liu,et al.  Image complexity and feature mining for steganalysis of least significant bit matching steganography , 2008, Inf. Sci..

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

[12]  John Hallam,et al.  IEEE International Joint Conference on Neural Networks , 2005 .

[13]  William A. Pearlman,et al.  Steganalysis of additive-noise modelable information hiding , 2003, IS&T/SPIE Electronic Imaging.

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

[15]  Phil Sallee,et al.  Model-Based Steganography , 2003, IWDW.

[16]  Alberto Leon-Garcia,et al.  Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[17]  Lisa M. Marvel,et al.  Spread spectrum image steganography , 1999, IEEE Trans. Image Process..

[18]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[19]  Siwei Lyu,et al.  How realistic is photorealistic? , 2005, IEEE Transactions on Signal Processing.

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

[21]  Yun Q. Shi,et al.  A Markov Process Based Approach to Effective Attacking JPEG Steganography , 2006, Information Hiding.

[22]  Tomás Pevný,et al.  Merging Markov and DCT features for multi-class JPEG steganalysis , 2007, Electronic Imaging.

[23]  Qingzhong Liu,et al.  Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images , 2008, Pattern Recognit..

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

[25]  I. Jolliffe.  Principal Component Analysis , 2002 .