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Neural networks are known to be vulnerable to adversarial examples. Carefullychosen perturbations to real images, while imperceptible to humans, inducemisclassification and threaten the reliability of deep learning systems in thewild. To guard against adversarial examples, we take inspiration from gametheory and cast the problem as a minimax zero-sum game between the adversaryand the model. In general, for such games, the optimal strategy for bothplayers requires a stochastic policy, also known as a mixed strategy. In thislight, we propose Stochastic Activation Pruning (SAP), a mixed strategy foradversarial defense. SAP prunes a random subset of activations (preferentiallypruning those with smaller magnitude) and scales up the survivors tocompensate. We can apply SAP to pretrained networks, including adversariallytrained models, without fine-tuning, providing robustness against adversarialexamples. Experiments demonstrate that SAP confers robustness against attacks,increasing accuracy and preserving calibration.

Stochastic Activation Pruning for Robust Adversarial Defense