Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization 事件

Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization arXiv:2606.12251v1 Announce Type: cross Abstract: Gradient-based adversarial attacks remain a dominant threat to deep neural networks (DNNs), as they exploit gradient information to efficiently optimize adversarial perturbations. To address this, we investigate whether reinforcement learning (RL) training can disrupt the gradient structure used by attackers by training image classifiers with policy-gradient objectives and e