293 citations · 1.5k across the 77 of their papers we have counts for
3 papers · 2 filters
Adversarial Attack Generation Empowered by Min-Max Optimization
Jingkang Wang, Tianyun Zhang, Sijia Liu +4
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing ad…
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
Xinlei Pan, Weiyao Wang, Xiaoshuai Zhang +3
Machine learning has been widely applied to various applications, some of which involve training with privacy-sensitive data. A modest number of data breaches have been studied, in…
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
Shawn Shan, Emily Wenger, Bolun Wang +3
Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or cost…