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cs.LG2025
Latent Danger Zone: Distilling Unified Attention for Cross-Architecture Black-box Attacks
Yang Li, Chenyu Wang, Tingrui Wang +4
Black-box adversarial attacks remain challenging due to limited access to model internals. Existing methods often depend on specific network architectures or require numerous queri…
cs.LG2023
PGN: A perturbation generation network against deep reinforcement learning
Xiangjuan Li, Feifan Li, Yang Li +1
Deep reinforcement learning has advanced greatly and applied in many areas. In this paper, we explore the vulnerability of deep reinforcement learning by proposing a novel generati…
cs.LG2022
Deep-Attack over the Deep Reinforcement Learning
Yang Li, Quan Pan, Erik Cambria
Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose…