Steganographic capacity estimation for the statistical restoration framework

In this paper we attempt to quantify the "active" steganographic capacity - the maximum rate at which data can be hidden, and correctly decoded, in a multimedia cover subject to noise/attack (hence - active), perceptual distortion criteria, and statistical steganalysis. Though work has been done in studying the capacity of data hiding as well as the rate of perfectly secure data hiding in noiseless channels, only very recently have all the constraints been considered together. In this work, we seek to provide practical estimates of steganographic capacity in natural images, undergoing realistic attacks, and using data hiding methods available today. We focus here on the capacity of an image data hiding channel characterized by the use of statistical restoration to satisfy the constraint of perfect security (under an i.i.d. assumption), as well as JPEG and JPEG-2000 attacks. Specifically we provide experimental results of the statistically secure hiding capacity on a set of several hundred images for hiding in a pre-selected band of frequencies, using the discrete cosine and wavelet transforms, where a perturbation of the quantized transform domain terms by ±1 using the quantization index modulation scheme, is considered to be perceptually transparent. Statistical security is with respect to the matching of marginal statistics of the quantized transform domain terms.

[1]  B. S. Manjunath,et al.  Determining Achievable Rates for Secure, Zero Divergence, Steganography , 2006, 2006 International Conference on Image Processing.

[2]  Anindya Sarkar,et al.  Estimating Steganographic Capacity for Odd-Even Based Embedding and its Use in Individual Compensation , 2007, 2007 IEEE International Conference on Image Processing.

[3]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[4]  B. S. Manjunath,et al.  Statistical restoration for robust and secure steganography , 2005, IEEE International Conference on Image Processing 2005.

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

[6]  Ying Wang,et al.  Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions , 2007, IEEE Transactions on Information Theory.

[7]  Joseph A. O'Sullivan,et al.  Information-theoretic analysis of information hiding , 2003, IEEE Trans. Inf. Theory.

[8]  B. S. Manjunath,et al.  Robust image-adaptive data hiding using erasure and error correction , 2004, IEEE Transactions on Image Processing.

[9]  B. S. Manjunath,et al.  Provably Secure Steganography: Achieving Zero K-L Divergence using Statistical Restoration , 2006, 2006 International Conference on Image Processing.

[10]  Young Huh,et al.  Wavelet transforms in a JPEG-like image coder , 1997, IEEE Trans. Circuits Syst. Video Technol..

[11]  Anindya Sarkar,et al.  Secure Steganography: Statistical Restoration of the Second Order Dependencies for Improved Security , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[12]  Christian Cachin,et al.  An information-theoretic model for steganography , 1998, Inf. Comput..

[13]  Yun Q. Shi,et al.  Statistical Moments Based Universal Steganalysis using JPEG 2-D Array and 2-D Characteristic Function , 2006, 2006 International Conference on Image Processing.

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

[15]  Dariush Divsalar,et al.  Coding theorems for 'turbo-like' codes , 1998 .

[16]  B. S. Manjunath,et al.  High-volume data hiding in images: Introducing perceptual criteria into quantization based embedding , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  Amos Lapidoth,et al.  The Gaussian watermarking game , 2000, IEEE Trans. Inf. Theory.

[18]  Gregory W. Wornell,et al.  Quantization index modulation: A class of provably good methods for digital watermarking and information embedding , 2001, IEEE Trans. Inf. Theory.

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