17 citations · 32 across the 8 of their papers we have counts for
8 papers
Securing Deep Generative Models with Universal Adversarial Signature
Yu Zeng, Mo Zhou, Yuan Xue +1
Recent advances in deep generative models have led to the development of methods capable of synthesizing high-quality, realistic images. These models pose threats to society due to…
On Trace of PGD-Like Adversarial Attacks
Mo Zhou, Vishal M. Patel
Adversarial attacks pose safety and security concerns to deep learning applications, but their characteristics are under-explored. Yet largely imperceptible, a strong trace could h…
Enhancing Adversarial Robustness for Deep Metric Learning
Mo Zhou, Vishal M. Patel
Owing to security implications of adversarial vulnerability, adversarial robustness of deep metric learning models has to be improved. In order to avoid model collapse due to exces…
Adversarial Attack and Defense in Deep Ranking
Mo Zhou, Le Wang, Zhenxing Niu +3
Deep Neural Network classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based…
SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
Liushuai Shi, Le Wang, Chengjiang Long +4
Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very challenging due to complex interactions between pedestrians. However, previous works bas…
Practical Relative Order Attack in Deep Ranking
Mo Zhou, Le Wang, Zhenxing Niu +4
Recent studies unveil the vulnerabilities of deep ranking models, where an imperceptible perturbation can trigger dramatic changes in the ranking result. While previous attempts fo…