88 citations · 171 across the 6 of their papers we have counts for
6 papers
Analyzing the Noise Robustness of Deep Neural Networks
Kelei Cao, Mengchen Liu, Hang Su +3
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to normal examples, can mislead deep neural networks (DNNs) to make incorrect predicti…
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…
Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork
Yulong Wang, Xiaolin Hu, Hang Su
We propose a novel perspective to understand deep neural networks in an interpretable disentanglement form. For each semantic class, we extract a class-specific functional subnetwo…
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…
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
Yinpeng Dong, Fan Bao, Hang Su +1
Sometimes it is not enough for a DNN to produce an outcome. For example, in applications such as healthcare, users need to understand the rationale of the decisions. Therefore, it…