6 papers
Toward Understanding Adversarial Distillation: Why Robust Teachers Fail
Hongsin Lee, Hye Won Chung
Adversarial Distillation aims to enhance student robustness by guiding the student with a robust teacher's soft labels within the min-max adversarial training framework, yet its su…
Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation
Hongsin Lee, Hye Won Chung
Adversarial distillation in the standard min-max adversarial training framework aims to transfer adversarial robustness from a large, robust teacher network to a compact student. H…
Long-tailed Adversarial Training with Self-Distillation
Seungju Cho, Hongsin Lee, Changick Kim
Adversarial training significantly enhances adversarial robustness, yet superior performance is predominantly achieved on balanced datasets. Addressing adversarial robustness in th…
Towards more transferable adversarial attack in black-box manner
Chun Tong Lei, Zhongliang Guo, Hon Chung Lee +2
Adversarial attacks have become a well-explored domain, frequently serving as evaluation baselines for model robustness. Among these, black-box attacks based on transferability hav…
Indirect Gradient Matching for Adversarial Robust Distillation
Hongsin Lee, Seungju Cho, Changick Kim
Adversarial training significantly improves adversarial robustness, but superior performance is primarily attained with large models. This substantial performance gap for smaller m…
Enhancing Robustness in Incremental Learning with Adversarial Training
Seungju Cho, Hongsin Lee, Changick Kim
Adversarial training is one of the most effective approaches against adversarial attacks. However, adversarial training has primarily been studied in scenarios where data for all c…