87 citations · 125 across the 10 of their papers we have counts for
4 papers · 1 filter
Decision-based Universal Adversarial Attack
Jing Wu, Mingyi Zhou, Shuaicheng Liu +2
A single perturbation can pose the most natural images to be misclassified by classifiers. In black-box setting, current universal adversarial attack methods utilize substitute mod…
ProbaNet: Proposal-balanced Network for Object Detection
Jing Wu, Xiang Zhang, Mingyi Zhou +1
Candidate object proposals generated by object detectors based on convolutional neural network (CNN) encounter easy-hard samples imbalance problem, which can affect overall perform…
Adversarial Imitation Attack
Mingyi Zhou, Jing Wu, Yipeng Liu +4
Deep learning models are known to be vulnerable to adversarial examples. A practical adversarial attack should require as little as possible knowledge of attacked models. Current s…
DaST: Data-free Substitute Training for Adversarial Attacks
Mingyi Zhou, Jing Wu, Yipeng Liu +2
Machine learning models are vulnerable to adversarial examples. For the black-box setting, current substitute attacks need pre-trained models to generate adversarial examples. Howe…