19 citations · 34 across the 3 of their papers we have counts for
4 papers
Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models
Zhibo Wang, Xiaowei Dong, Henry Xue +4
Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e.g., hiring systems should recommend applicants…
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…