Advancing Adversarial Robustness Through Adversarial Logit Update
Hao Xuan Peican Zhu Xingyu Li
Deep Neural Networks are susceptible to adversarial perturbations.
Adversarial training and adversarial purification are among the most widely
recognized defense strategies. Although these methods have different underlying
logic, both rely on absolute logit values to generate label predictions. In
this study, we theoretically analyze the logit difference around successful
adversarial attacks from a theoretical point of view and propose a new
principle, namely Adversarial Logit Update (ALU), to infer adversarial sample's
labels. Based on ALU, we introduce a new classification paradigm that utilizes
pre- and post-purification logit differences for model's adversarial robustness
boost. Without requiring adversarial or additional data for model training, our
clean data synthesis model can be easily applied to various pre-trained models
for both adversarial sample detection and ALU-based data classification.
Extensive experiments on both CIFAR-10, CIFAR-100, and tiny-ImageNet datasets
show that even with simple components, the proposed solution achieves superior
robustness performance compared to state-of-the-art methods against a wide
range of adversarial attacks. Our python implementation is submitted in our
Supplementary document and will be published upon the paper's acceptance.