Blind Statistical Steganalysis of Additive Steganography Using Wavelet Higher Order Statistics

Development of digital communications systems significantly extended possibility to perform covert communications (steganography). This recalls an emerging demand in highly efficient counter-measures, i.e. steganalysis methods. Modern steganography is presented by a broad spectrum of various data-hiding techniques. Therefore development of corresponding steganalysis methods is rather a complex problem and challenging task. Moreover, in many practical steganalysis tasks second Kerckhoff's principle is not applicable because of absence of information about the used steganography method. This motivates to use blind steganalysis, which can be applied to the certain techniques where one can specify at least statistics of the hidden data. This paper focuses on the class of supervised steganalysis techniques developed for the additive steganography, which can be described as y = f(x, s, K) = x + g(s, K), where stego image y is obtained from the cover image x by adding a low-amplitude cover image independent ((1 embedding also known as LSB matching) or cover image dependent (LSB embedding) stego signals that may be also depended on secret stego key K and the secret data s. The function g(.) represents the embedding rule.

[1]  Jessica J. Fridrich,et al.  Stochastic approach to secret message length estimation in ±k embedding steganography , 2005, IS&T/SPIE Electronic Imaging.

[2]  André Kaup,et al.  Steganographic system based on higher-order statistics , 2003, IS&T/SPIE Electronic Imaging.

[3]  Kannan Ramchandran,et al.  Low-complexity image denoising based on statistical modeling of wavelet coefficients , 1999, IEEE Signal Processing Letters.

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

[5]  Andreas Westfeld.  Detecting Low Embedding Rates , 2002, Information Hiding.

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

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

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

[9]  Michael T. Orchard,et al.  Image coding based on mixture modeling of wavelet coefficients and a fast estimation-quantization framework , 1997, Proceedings DCC '97. Data Compression Conference.

[10]  Andrew D. Ker.  Steganalysis of LSB matching in grayscale images , 2005, IEEE Signal Processing Letters.

[11]  Jessica J. Fridrich,et al.  Digital image steganography using stochastic modulation , 2003, IS&T/SPIE Electronic Imaging.

[12]  Sorina Dumitrescu,et al.  Detection of LSB steganography via sample pair analysis , 2002, IEEE Trans. Signal Process..

[13]  Miroslav Goljan,et al.  Steganalysis based on JPEG compatibility , 2001, SPIE ITCom.

[14]  A. Murat Tekalp,et al.  Universal image steganalysis using rate-distortion curves , 2004, IS&T/SPIE Electronic Imaging.

[15]  Toby Sharp,et al.  An Implementation of Key-Based Digital Signal Steganography , 2001, Information Hiding.

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

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

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

[19]  Nasir D. Memon,et al.  Steganalysis using image quality metrics , 2003, IEEE Trans. Image Process..