16 citations · 46 across the 8 of their papers we have counts for
7 papers · 1 filter
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks
Chen Ma, Xiangyu Guo, Li Chen +2
One major problem in black-box adversarial attacks is the high query complexity in the hard-label attack setting, where only the top-1 predicted label is available. In this paper,…
Improving Robustness for Pose Estimation via Stable Heatmap Regression
Yumeng Zhang, Li Chen, Yufeng Liu +3
Deep learning methods have achieved excellent performance in pose estimation, but the lack of robustness causes the keypoints to change drastically between similar images. In view…
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks
Chen Ma, Shuyu Cheng, Li Chen +2
We propose a simple and highly query-efficient black-box adversarial attack named SWITCH, which has a state-of-the-art performance in the score-based setting. SWITCH features a hig…
Simulating Unknown Target Models for Query-Efficient Black-box Attacks
Chen Ma, Li Chen, Jun-Hai Yong
Many adversarial attacks have been proposed to investigate the security issues of deep neural networks. In the black-box setting, current model stealing attacks train a substitute…
Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks
Yumeng Zhang, Gaoguo Jia, Li Chen +2
There is an urgent need for an effective video classification method by means of a small number of samples. The deficiency of samples could be effectively alleviated by generating…
Adaptive Wasserstein Hourglass for Weakly Supervised Hand Pose Estimation from Monocular RGB
Yumeng Zhang, Li Chen, Yufeng Liu +2
Insufficient labeled training datasets is one of the bottlenecks of 3D hand pose estimation from monocular RGB images. Synthetic datasets have a large number of images with precise…
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks
Chen Ma, Chenxu Zhao, Hailin Shi +3
Deep neural networks (DNNs) are vulnerable to adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect p…