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CNN-based steganalysis has recently achieved very good performance indetecting content-adaptive steganography. At the same time, recent works haveshown that, by adopting an approach similar to that used to build adversarialexamples, a steganographer can adopt an adversarial embedding strategy toeffectively counter a target CNN steganalyzer. In turn, the good performance ofthe steganalyzer can be restored by retraining the CNN with adversarial stegoimages. A problem with this model is that, arguably, at training time thesteganalizer is not aware of the exact parameters used by the steganograher foradversarial embedding and, vice versa, the steganographer does not know how theimages that will be used to train the steganalyzer are generated. In order toexit this apparent deadlock, we introduce a game theoretic framework whereinthe problem of setting the parameters of the steganalyzer and thesteganographer is solved in a strategic way. More specifically, a non-zero sumgame is first formulated to model the problem, and then instantiated byconsidering a specific adversarial embedding scheme setting its operatingparameters in a game-theoretic fashion. Our analysis shows that the equilibriumsolution of the non zero-sum game can be conveniently found by solving anassociated zero-sum game, thus reducing greatly the complexity of the problem.Then we run several experiments to derive the optimum strategies for thesteganographer and the staganalyst in a game-theoretic sense, and to evaluatethe performance of the game at the equilibrium, characterizing the loss withrespect to the conventional non-adversarial case. Eventually, by leveraging onthe analysis of the equilibrium point of the game, we introduce a new strategyto improve the reliability of the steganalysis, which shows the benefits ofaddressing the security issue in a game-theoretic perspective.

CNN-based Steganalysis and Parametric Adversarial Embedding: a Game-Theoretic Framework