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We then propose a set of retraining-free ``gradient inhibition'' (GI) methods to extremely suppress and randomize the gradient used to craft adversarial examples. Finally, we develop a comprehensive defense framework by orchestrating ``defensive hash classifier'' and ``GI.'' We evaluate our defense across traditional white-box, strong adaptive white-box, and black-box settings. Extensive studies show that our solution can enormously decrease the attack success rate of various adversarial attacks on the diverse dataset.Reinforcement learn
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