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Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks
Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations.
Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization
Moreover, to ameliorate the phenomenon of sub-optimization with one fixed style, we propose to discover the optimal style given a target through style optimization in a continuous relaxation manner.
Reducing Adversarial Training Cost with Gradient Approximation
Deep learning models have achieved state-of-the-art performances in various domains, while they are vulnerable to the inputs with well-crafted but small perturbations, which are named after adversarial examples (AEs).
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