3 accepted papers
Deep neural networks are vulnerable to adversarial attacks leading to erroneous outputs. Adversarial training has been recognized as one of the most effective methods to counter such attacks. However existing adversarial training techniques have predominantly been evaluated on balanced datasets wher…
Adversarial Robustness Distillation (ARD) aims to transfer the robustness of large teacher models to small student models, facilitating the attainment of robust performance on resource-limited devices. However, existing research on ARD primarily focuses on the overall robustness of student models, o…
Previous researches have demonstrated deep learning models' vulnerabilities to decision-based adversarial attacks, which craft adversarial examples based solely on information from output decisions (top-1 labels). However, existing decision-based attacks have two major limitations, i.e., expensive q…