19 citations · 50 across the 7 of their papers we have counts for
4 papers · 1 filter
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing
Dou Goodman, Xingjian Li, Ji Liu +2
Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversa…
Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield
Dou Goodman, Tao Wei
Many recent works demonstrated that Deep Learning models are vulnerable to adversarial examples.Fortunately, generating adversarial examples usually requires white-box access to th…
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking
Yunhan Jia, Yantao Lu, Junjie Shen +3
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models. Howe…
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
Yunhan Jia, Yantao Lu, Senem Velipasalar +2
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they maintain their effectiveness even again…