The Source Identification Game: An Information-Theoretic Perspective

Abstract

We introduce a theoretical framework in which to cast the source identification problem. Thanks to the adoption of a game-theoretic approach, the proposed framework permits us to derive the ultimate achievable performance of the forensic analysis in the presence of an adversary aiming at deceiving it. The asymptotic Nash equilibrium of the source identification game is derived under an assumption on the resources on which the forensic analyst may rely. The payoff at the equilibrium is an- alyzed, deriving the conditions under which a successful forensic analysis is possible and the error exponent of the false-negative error probability in such a case. The difficulty of deriving a closed-form solution for general instances of the game is alleviated by the introduction of an efficient numerical procedure for the derivation of the optimum attacking strategy. The numerical analysis is applied to a case study to show the kind of information it can provide

The role of permutation coding in minimum-distortion perfect counterforensics

“…If the database is large then it is likely that a good match will be found. A procedure is given in [3] for obtaining y from x and z, based on histogram and pixel remapping techniques related to transportation theory [10]; this method is able to enforce a target distortion, at the cost of an imperfect post-processed forgery y. However it can always be replaced by the method in Section 2 to get a minimum-distortion perfect post-processed forgery, which is normally very faithful due the huge space of possibilities in the combinatorial optimisation.…”

Coping with the enemy: Advances in adversary-aware signal processing

“…By referring to MF, some early attempts to define a general framework for adversarial hypothesis testing have been made, including [2,17], where game-theory and information theory are used to derive the Nash equilibrium point of the source identification game, and [3] where the KullbackLeibler distance is used to measure the validity of an attack regardless of the adopted countermeasures. Similar attempts have been carried out in the field of adversarial machine learning (see [5] for a review).…”

“…the defender, and the adversary, is through game theory. In the following we use the approach outlined in [2] to show how game theory can be used to formulate a general binary decision game, whose analysis can shed new light on the limits and achievable performance of binary decision under adversarial conditions.…”

“…Some scattered steps in the right direction have already been taken: watermarking security is now clearly distinguished from watermark robustness [1]; multimedia forensics in the presence of an adversary has raised the attention of researchers and counter-forensics (and even counter-counter forensics) techniques are now studied [2,3]; the study of antispoofing techniques is an active research field [4]; adversaryaware machine learning is investigated with applications to spam filtering, network intrusion detection and malware detection [5,6]. It is the aim of this paper to review the most recent advances in the fields where signal processing designers have to cope with the presence of an adversary, highlighting the similarities between the existing approaches, and provide a unitary view for one of the most commonly encountered problems, namely binary decision.…”

Botnet identification in multi-clustered DDoS attacks

“…Unfortunately, the aforementioned strategies are not suited to face the novel class of randomized DDoS attacks. To overcome this issue, in [1], [2] a botnet identification algorithm is proposed, which lies somehow between the two extremes of fully-parametric [10]- [12], and fully-data-driven [13] approaches. Following emerging trends in signal processing for network cyber-security [14]- [17], in [1], [2] consistent botnet identification is achieved through descriptive indicators (i.e., the message innovation rate and the emulation dictionary rate) that arise from a minimal set of physical assumptions.…”