224 citations · 3.2k across the 170 of their papers we have counts for
6 papers · 2 filters
Benchmarking Adversarial Robustness
Yinpeng Dong, Qi-An Fu, Xiao Yang +4
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts h…
Design and Interpretation of Universal Adversarial Patches in Face Detection
Xiao Yang, Fangyun Wei, Hongyang Zhang +1
We consider universal adversarial patches for faces -- small visual elements whose addition to a face image reliably destroys the performance of face detectors. Unlike previous wor…
Countering Noisy Labels By Learning From Auxiliary Clean Labels
Tsung Wei Tsai, Chongxuan Li, Jun Zhu
We consider the learning from noisy labels (NL) problem which emerges in many real-world applications. In addition to the widely-studied synthetic noise in the NL literature, we al…
Efficient Decision-based Black-box Adversarial Attacks on Face Recognition
Yinpeng Dong, Hang Su, Baoyuan Wu +4
Face recognition has obtained remarkable progress in recent years due to the great improvement of deep convolutional neural networks (CNNs). However, deep CNNs are vulnerable to ad…
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Yinpeng Dong, Tianyu Pang, Hang Su +1
Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is…
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Zhijie Deng, Yucen Luo, Jun Zhu
Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain wit…