Efficient Adversarial Training With Data Pruning

• URL: http://arxiv.org/abs/2207.00694v1
• Date: Fri, 1 Jul 2022 23:54:46 GMT
• Title: Efficient Adversarial Training With Data Pruning
• Authors: Maximilian Kaufmann, Yiren Zhao, Ilia Shumailov, Robert Mullins and Nicolas Papernot
• Abstract summary: We show that data pruning leads to improvements in convergence and reliability of adversarial training. In some settings data pruning brings benefits from both worlds-it both improves adversarial accuracy and training time.
• Score: 26.842714298874192
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Neural networks are susceptible to adversarial examples-small input perturbations that cause models to fail. Adversarial training is one of the solutions that stops adversarial examples; models are exposed to attacks during training and learn to be resilient to them. Yet, such a procedure is currently expensive-it takes a long time to produce and train models with adversarial samples, and, what is worse, it occasionally fails. In this paper we demonstrate data pruning-a method for increasing adversarial training efficiency through data sub-sampling.We empirically show that data pruning leads to improvements in convergence and reliability of adversarial training, albeit with different levels of utility degradation. For example, we observe that using random sub-sampling of CIFAR10 to drop 40% of data, we lose 8% adversarial accuracy against the strongest attackers, while by using only 20% of data we lose 14% adversarial accuracy and reduce runtime by a factor of 3. Interestingly, we discover that in some settings data pruning brings benefits from both worlds-it both improves adversarial accuracy and training time.

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