Multi-class Blind Steganalysis Based on Image Run-Length Analysis

In this paper, we investigate our previously developed run-length based features for multi-class blind image steganalysis. We construct a Support Vector Machine classifier for multi-class recognition for both spatial and frequency domain based steganographic algorithms. We also study hierarchical and non-hierarchical multi-class schemes and compare their performance for steganalysis. Experimental results demonstrate that our approach is able to classify different stego images according to their embedding techniques based on appropriate supervised learning. It is also shown that the hierarchical scheme performs better in our experiments.

[1]  Jessica J. Fridrich,et al.  Quantitative steganalysis of digital images: estimating the secret message length , 2003, Multimedia Systems.

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[3]  Anindya Sarkar,et al.  YASS: Yet Another Steganographic Scheme That Resists Blind Steganalysis , 2007, Information Hiding.

[4]  Andreas Westfeld,et al.  F5—A Steganographic Algorithm High Capacity Despite Better Steganalysis , 2001 .

[5]  Jessica J. Fridrich,et al.  Practical steganalysis of digital images: state of the art , 2002, IS&T/SPIE Electronic Imaging.

[6]  Tomás Pevný,et al.  Towards Multi-class Blind Steganalyzer for JPEG Images , 2005, IWDW.

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

[8]  Ingemar J. Cox,et al.  Digital Watermarking , 2003, Lecture Notes in Computer Science.

[9]  Jessica Fridrich,et al.  Determining the stego algorithm for JPEG images , 2006 .

[10]  Jing Dong,et al.  Blind image steganalysis based on run-length histogram analysis , 2008, 2008 15th IEEE International Conference on Image Processing.

[11]  Fenlin Liu,et al.  Multi-class steganalysis for Jpeg stego algorithms , 2008, 2008 15th IEEE International Conference on Image Processing.

[12]  Gerald Schaefer,et al.  UCID: an uncompressed color image database , 2003, IS&T/SPIE Electronic Imaging.

[13]  Nasir D. Memon,et al.  Benchmarking steganographic and steganalysis techniques , 2005, IS&T/SPIE Electronic Imaging.

[14]  Andrew D. Ker.  Resampling and the detection of LSB matching in color bitmaps , 2005, IS&T/SPIE Electronic Imaging.

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

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

[17]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

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

[19]  Sorina Dumitrescu,et al.  Detection of LSB Steganography via Sample Pair Analysis , 2002, Information Hiding.

[20]  Ingemar J. Cox,et al.  Secure spread spectrum watermarking for multimedia , 1997, IEEE Trans. Image Process..

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

[22]  Chengyun Yang,et al.  Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network , 2005, 2005 IEEE International Conference on Multimedia and Expo.

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