Browsing by Author "Tan, Renhao"

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    Constraining Robust Information Quantities Improves Adversarial Robustness
    (University of Waterloo, 2024-12-11) Tan, Renhao
    It is known that deep neural networks (DNNs) are vulnerable to imperceptible adversarial attacks, and this fact raises concerns about their safety and reliability in real-world applications. In this thesis, we aim to boost the robustness of DNNs against white-box adversarial attacks by defining three information quantities: robust conditional mutual information (CMI), robust separation, and robust normalized CMI (NCMI), which can serve as evaluation metrics of robust performance for a DNN. We then utilize these concepts to introduce a novel regularization method that constrains intra-class concentration and increases inter-class separation simultaneously among output probability distributions of attacked data. Our experimental results demonstrate that our method consistently enhances model robustness against C&W and AutoAttack on CIFAR and Tiny-ImageNet datasets, both with and without additional synthetic data. The results show that our approach enhances the robust accuracy of DNNs by up to 2.66% on CIFAR datasets and 3.49% on Tiny-ImageNet against PGD attacks, and by 1.70% on CIFAR and 1.63% on Tiny-ImageNet against AutoAttack, compared to several state-of-the-art adversarial training methods.