Adversarial attacks can generate adversarial inputs by applying small but
intentionally worst-case perturbations to samples from the dataset, which leads
to even state-of-the-art deep neural networks outputting incorrect answers with
high confidence. Hence, some adversarial defense techniques are developed to
improve the security and robustness of the models and avoid them being
attacked. Gradually, a game-like competition between attackers and defenders
formed, in which both players would attempt to play their best strategies
against each other while maximizing their own payoffs. To solve the game, each
player would choose an optimal strategy against the opponent based on the
prediction of the opponent’s strategy choice. In this work, we are on the
defensive side to apply game-theoretic approaches on defending against attacks.
We use two randomization methods, random initialization and stochastic
activation pruning, to create diversity of networks. Furthermore, we use one
denoising technique, super resolution, to improve models’ robustness by
preprocessing images before attacks. Our experimental results indicate that
those three methods can effectively improve the robustness of deep-learning
neural networks.

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