19 citations · 50 across the 7 of their papers we have counts for
7 papers
Black-box Dataset Ownership Verification via Backdoor Watermarking
Yiming Li, Mingyan Zhu, Xue Yang +3
Deep learning, especially deep neural networks (DNNs), has been widely and successfully adopted in many critical applications for its high effectiveness and efficiency. The rapid d…
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…
Practical Privacy Attacks on Vertical Federated Learning
Haiqin Weng, Juntao Zhang, Xingjun Ma +4
Federated learning (FL) is a privacy-preserving learning paradigm that allows multiple parities to jointly train a powerful machine learning model without sharing their private dat…
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…