  CNN-based steganalysis has recently achieved very good performance in
detecting content-adaptive steganography. At the same time, recent works have
shown that, by adopting an approach similar to that used to build adversarial
examples, a steganographer can adopt an adversarial embedding strategy to
effectively counter a target CNN steganalyzer. In turn, the good performance of
the steganalyzer can be restored by retraining the CNN with adversarial stego
images. A problem with this model is that, arguably, at training time the
steganalizer is not aware of the exact parameters used by the steganograher for
adversarial embedding and, vice versa, the steganographer does not know how the
images that will be used to train the steganalyzer are generated. In order to
exit this apparent deadlock, we introduce a game theoretic framework wherein
the problem of setting the parameters of the steganalyzer and the
steganographer is solved in a strategic way. More specifically, a non-zero sum
game is first formulated to model the problem, and then instantiated by
considering a specific adversarial embedding scheme setting its operating
parameters in a game-theoretic fashion. Our analysis shows that the equilibrium
solution of the non zero-sum game can be conveniently found by solving an
associated zero-sum game, thus reducing greatly the complexity of the problem.
Then we run several experiments to derive the optimum strategies for the
steganographer and the staganalyst in a game-theoretic sense, and to evaluate
the performance of the game at the equilibrium, characterizing the loss with
respect to the conventional non-adversarial case. Eventually, by leveraging on
the analysis of the equilibrium point of the game, we introduce a new strategy
to improve the reliability of the steganalysis, which shows the benefits of
addressing the security issue in a game-theoretic perspective.
