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cs.CV2023
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…
cs.CV2023
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…
cs.CV2023
Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup
Junyoung Byun, Myung-Joon Kwon, Seungju Cho +2
Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications. These adversarial example…