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Stochastic Activation Pruning for Robust Adversarial Defense

Dhillon, Guneet S. and Azizzadenesheli, Kamyar and Lipton, Zachary C. and Bernstein, Jeremy and Kossaifi, Jean and Khanna, Aran and Anandkumar, Anima (2018) Stochastic Activation Pruning for Robust Adversarial Defense. In: 6th International Conference on Learning Representations (ICLR 2018), 30 April-3 May 2018, Vancouver, Canada.

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Neural networks are known to be vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild. To guard against adversarial examples, we take inspiration from game theory and cast the problem as a minimax zero-sum game between the adversary and the model. In general, for such games, the optimal strategy for both players requires a stochastic policy, also known as a mixed strategy. In this light, we propose Stochastic Activation Pruning (SAP), a mixed strategy for adversarial defense. SAP prunes a random subset of activations (preferentially pruning those with smaller magnitude) and scales up the survivors to compensate. We can apply SAP to pretrained networks, including adversarially trained models, without fine-tuning, providing robustness against adversarial examples. Experiments demonstrate that SAP confers robustness against attacks, increasing accuracy and preserving calibration.

Item Type:Conference or Workshop Item (Poster)
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URLURL TypeDescription
Azizzadenesheli, Kamyar0000-0001-8507-1868
Lipton, Zachary C.0000-0002-3824-4241
Bernstein, Jeremy0000-0001-9110-7476
Kossaifi, Jean0000-0002-4445-3429
Anandkumar, Anima0000-0002-6974-6797
Record Number:CaltechAUTHORS:20190327-085749625
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94174
Deposited By: George Porter
Deposited On:29 Mar 2019 20:08
Last Modified:02 Jun 2023 00:38

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