Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigation

1 code implementation • 12 Jun 2022 • Chengyang Ying, You Qiaoben, Xinning Zhou, Hang Su, Wenbo Ding, Jianyong Ai

Among different adversarial noises, universal adversarial perturbations (UAP), i. e., a constant image-agnostic perturbation applied on every input frame of the agent, play a critical role in Embodied Vision Navigation since they are computation-efficient and application-practical during the attack.

Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning

no code implementations • 30 Jun 2021 • You Qiaoben, Chengyang Ying, Xinning Zhou, Hang Su, Jun Zhu, Bo Zhang

In this paper, we provide a framework to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space.

reinforcement-learning Reinforcement Learning (RL)

Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents

no code implementations • ICML Workshop AML 2021 • You Qiaoben, Xinning Zhou, Chengyang Ying, Jun Zhu

Deep reinforcement learning (DRL) policies are vulnerable to the adversarial attack on their observations, which may mislead real-world RL agents to catastrophic failures.

Adversarial Attack Continuous Control +2

Composite Binary Decomposition Networks

no code implementations • 16 Nov 2018 • You Qiaoben, Zheng Wang, Jianguo Li, Yinpeng Dong, Yu-Gang Jiang, Jun Zhu

Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the full-precision counterparts.

General Classification Image Classification +3