Towards Black-Box Adversarial Attacks on Interpretable Deep Learning Systems. - Yike Zhan; Baolin Zheng; Qian Wang 002; Ningping Mou; Binqing Guo; Qi Li 002; Chao Shen 001; Cong Wang 001 (ICME, 2022)

A Few Seconds Can Change Everything: Fast Decision-based Attacks against DNNs. - Ningping Mou; Baolin Zheng; Qian Wang 002; Yunjie Ge; Binqing Guo (IJCAI, 2022)

Improving Adversarial Transferability with Ghost Samples. - Yi Zhao 011; Ningping Mou; Yunjie Ge; Qian Wang 002 (ECAI, 2023)

Revisiting Adversarial Robustness Distillation from the Perspective of Robust Fairness. - Xinli Yue; Ningping Mou; Qian Wang 002; Lingchen Zhao (NeurIPS, 2023)

Revisiting Adversarial Training Under Long-Tailed Distributions. - Xinli Yue; Ningping Mou; Qian Wang 002; Lingchen Zhao (CVPR, 2024)

More Simplicity for Trainers, More Opportunity for Attackers: Black-Box Attacks on Speaker Recognition Systems by Inferring Feature Extractor. - Yunjie Ge; Pinji Chen; Qian Wang 002; Lingchen Zhao; Ningping Mou; Peipei Jiang 002; Cong Wang 001; Qi Li 002; Chao Shen 001 (USENIX Security Symposium, 2024)

No-Box Universal Adversarial Perturbations Against Image Classifiers via Artificial Textures. - Ningping Mou; Binqing Guo; Lingchen Zhao; Cong Wang 001; Yue Zhao; Qian Wang 002 (IEEE Trans. Inf. Forensics Secur., 2024)

Fairness is essential for robustness: fair adversarial training by identifying and augmenting hard examples. - Ningping Mou; Xinli Yue; Lingchen Zhao; Qian Wang 002 (Frontiers Comput. Sci., 2025)