基于CNN的隐写分析和参数对抗嵌入：一个博弈理论框架
CNN-based Steganalysis and Parametric Adversarial Embedding: a Game-Theoretic Framework
摘要：
最近，基于CNN的隐写分析在检测内容自适应隐写方面取得了很好的性能。与此同时，最近的工作表明，通过采用类似于用于构建对抗性示例的方法，隐写器可以采用对抗性嵌入策略来有效地对抗目标CNN隐写分析器。反过来，隐写分析器的良好性能可以通过用对抗性隐写图像重新训练CNN来恢复。该模型的一个问题是，可以说，在训练时隐写器不知道隐写器用于对抗嵌入的确切参数，反之亦然，隐写器不知道将用于训练隐写器的图像是如何生成的。为了退出这一明显的僵局，我们引入了一个博弈论框架，其中的隐写分析器和隐写器的参数设置的问题是解决在一个战略性的方式。更具体地说，首先制定一个非零和游戏来模拟这个问题，然后通过考虑一个特定的对抗嵌入方案来实例化，该方案以博弈论的方式设置其操作参数。分析表明，非零和博弈的均衡解可以通过求解一个相关的零和博弈来方便地求出，从而大大降低了问题的复杂性。然后，我们运行几个实验，以获得最佳策略的隐写和staganalysts在博弈论的意义上，并评估在均衡的游戏的性能，相对于传统的非对抗性的情况下的损失的特征。最后，通过对博弈均衡点的分析，我们引入了一种新的策略来提高隐写分析的可靠性，这表明了从博弈论的角度来解决安全问题的好处。
英文摘要：
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.
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