25 citations · 63 across the 7 of their papers we have counts for
7 papers
ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-based Systems
Mingyi Zhou, Xiang Gao, Jing Wu +4
More and more edge devices and mobile apps are leveraging deep learning (DL) capabilities. Deploying such models on devices -- referred to as on-device models -- rather than as rem…
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions
Jing Wu, Mingyi Zhou, Ce Zhu +3
Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models. However, mainstream evaluation criteria experience limitations, eve…
Local Label Point Correction for Edge Detection of Overlapping Cervical Cells
Jiawei Liu, Huijie Fan, Qiang Wang +5
Accurate labeling is essential for supervised deep learning methods. However, it is almost impossible to accurately and manually annotate thousands of images, which results in many…
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…