Transportation-theoretic image counterforensics to First Significant Digit histogram forensics

First-order statistics of First Significant Digits (FSD) have been recently exploited in multimedia forensics as a powerful tool to reveal traces of previous coding operations. As an answer, adversarial approaches aimed at modifying the FSD histogram and fooling such forensic methods have been proposed. However, the existing techniques have limitations in terms of distortion introduced in the multimedia object. In this paper, a transportation-theoretic formulation of the problem is presented which provides a close-to-optimal solution. Such strategy is tested in a well-known image forensic scenario, where FSDs of 8 × 8-DCT coefficients after single or double quantization are modified in order to restore a certain target histogram and the distortion with respect to the provided compressed image is measured in terms of MSE.

[1]  K. J. Ray Liu,et al.  Anti-forensics of digital image compression , 2011, IEEE Transactions on Information Forensics and Security.

[2]  Bin Li,et al.  Detecting doubly compressed JPEG images by using Mode Based First Digit Features , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[3]  S. Rachev,et al.  Mass transportation problems , 1998 .

[4]  Fan Zhang,et al.  Resource Allocation for Delay Differentiated Traffic in Multiuser OFDM Systems , 2006, IEEE Transactions on Wireless Communications.

[5]  Rainer Böhme,et al.  Can we trust digital image forensics? , 2007, ACM Multimedia.

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

[7]  Wei Su,et al.  A generalized Benford's law for JPEG coefficients and its applications in image forensics , 2007, Electronic Imaging.

[8]  Francesco G. B. De Natale,et al.  Counter-forensics of median filtering , 2013, 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP).

[9]  Fernando Pérez-González,et al.  Coping with the enemy: Advances in adversary-aware signal processing , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Giulia Boato,et al.  JPEG compression anti-forensics based on first significant digit distribution , 2013, 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP).

[11]  Stefano Tubaro,et al.  Antiforensics attacks to Benford's law for the detection of double compressed images , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Jean-Michel Jolion,et al.  Images and Benford's Law , 2001, Journal of Mathematical Imaging and Vision.

[13]  Mauro Barni,et al.  The Source Identification Game: An Information-Theoretic Perspective , 2013, IEEE Transactions on Information Forensics and Security.

[14]  Rainer Böhme,et al.  Hiding Traces of Resampling in Digital Images , 2008, IEEE Transactions on Information Forensics and Security.

[15]  Fernando Pérez-González,et al.  BENFORD ’ S LAW IN IMAGE PROCESSING , 2007 .

[16]  Rainer Böhme,et al.  Counter-Forensics: Attacking Image Forensics , 2013 .

[17]  Mauro Barni,et al.  A universal technique to hide traces of histogram-based image manipulations , 2012, MM&Sec '12.

[18]  Mauro Barni,et al.  Hiding traces of median filtering in digital images , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[19]  Fernando Pérez-González,et al.  Benford's Lawin Image Processing , 2007, 2007 IEEE International Conference on Image Processing.

[20]  Stefano Tubaro,et al.  Discriminating multiple JPEG compression using first digit features , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[21]  Fernando Pérez-González,et al.  Optimal counterforensics for histogram-based forensics , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.