MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
This work addresses the problem of adversarial attacks on deep learning systems, offering an incremental improvement in defense mechanisms.
The paper tackles the vulnerability of deep neural networks to adversarial attacks by proposing a multi-strength adversarial training method (MAT) that combines examples with different perturbation levels, resulting in substantially minimized accuracy degradation on datasets like MNIST, CIFAR-10, CIFAR-100, and SVHN.
Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.