The Security Margin: A measure of source distinguishability under adversarial conditions

We analyze the distinguishability of two sources under adversarial conditions, when the error exponents of type I and type II error probabilities are allowed to take an arbitrarily small, yet positive, values. By exploiting the parallelism between the attacker's goal and optimal transport theory, we introduce the concept of Security Margin defined as the maximum average per-sample distortion introduced by the attacker for which the two sources can be reliably distinguished. We compute the security margin for some classes of sources and derive a general upper bound which is valid for any kind of sources assuming that the distortion is measured in terms of the mean square error between the original and the attacked sequences.

[1]  L. Billard,et al.  Mallows' L2 distance in some multivariate methods and its application to histogram-type data , 2012, Advances in Methodology and Statistics.

[2]  Peter J. Bickel,et al.  The Earth Mover's distance is the Mallows distance: some insights from statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[3]  C. Villani Optimal Transport: Old and New , 2008 .

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

[5]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[6]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[7]  Mauro Barni,et al.  Optimum forensic and counter-forensic strategies for source identification with training data , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).

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

[9]  Antonio Irpino,et al.  Optimal histogram representation of large data sets: Fisher vs piecewise linear approximation , 2007, EGC.

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

[11]  Imre Csisźar,et al.  The Method of Types , 1998, IEEE Trans. Inf. Theory.

[12]  Ngo Van Long,et al.  Iterated Strict Dominance in General Games , 2007, Games Econ. Behav..

[13]  Michael Werman,et al.  Fast and robust Earth Mover's Distances , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[14]  Mauro Barni,et al.  A game theoretic approach to source identification with known statistics , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).