Accelerating Adversarial Perturbation by 50 Propagation

11/09/2022

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by   Zhiqi Bu, et al.

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Adversarial perturbation plays a significant role in the field of adversarial robustness, which solves a maximization problem over the input data. We show that the backward propagation of such optimization can accelerate 2× (and thus the overall optimization including the forward propagation can accelerate 1.5×), without any utility drop, if we only compute the output gradient but not the parameter gradient during the backward propagation.