Towards Understanding the Generative Capability of Adversarially Robust Classifiers. - Yao Zhu 003; Jiacheng Ma 004; Jiacheng Sun; Zewei Chen; Rongxin Jiang 001; Yaowu Chen; Zhenguo Li (ICCV, 2021)

Towards Understanding the Generative Capability of Adversarially Robust Classifiers. - Yao Zhu 003; Jiacheng Ma 004; Jiacheng Sun; Zewei Chen; Rongxin Jiang 001; Zhenguo Li (CoRR, 2021)

Enhance the Visual Representation via Discrete Adversarial Training. - Xiaofeng Mao; Yuefeng Chen; Ranjie Duan; Yao Zhu 003; Gege Qi; Shaokai Ye; Xiaodan Li; Rong Zhang 006; Hui Xue 001 (NeurIPS, 2022)

Boosting Out-of-distribution Detection with Typical Features. - Yao Zhu 003; Yuefeng Chen; Chuanlong Xie; Xiaodan Li; Rong Zhang 006; Hui Xue 001; Xiang Tian 002; Bolun Zheng; Yaowu Chen (NeurIPS, 2022)

Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective. - Yao Zhu 003; Yuefeng Chen; Xiaodan Li; Kejiang Chen; Yuan He 011; Xiang Tian 002; Bolun Zheng; Yaowu Chen; Qingming Huang (CoRR, 2022)

Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective. - Yao Zhu 003; Yuefeng Chen; Xiaodan Li; Kejiang Chen; Yuan He 011; Xiang Tian 002; Bolun Zheng; Yaowu Chen; Qingming Huang (IEEE Trans. Image Process., 2022)

ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing. - Xiaodan Li; Yuefeng Chen; Yao Zhu 003; Shuhui Wang; Rong Zhang 006; Hui Xue 001 (CVPR, 2023)

Transaudio: Towards the Transferable Adversarial Audio Attack Via Learning Contextualized Perturbations. - Gege Qi; Yuefeng Chen; Yao Zhu 003; Binyuan Hui; Xiaodan Li; Xiaofeng Mao; Rong Zhang 006; Hui Xue 001 (ICASSP, 2023)

COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts. - Xiaofeng Mao; Yuefeng Chen; Yao Zhu 003; Da Chen 003; Hang Su 006; Rong Zhang 006; Hui Xue 001 (ICCV, 2023)

Inequality phenomenon in l∞-adversarial training, and its unrealized threats. - Ranjie Duan; Yuefeng Chen; Yao Zhu 003; Xiaojun Jia; Rong Zhang 006; Hui Xue 001 (ICLR, 2023)

Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning. - Yao Zhu 003; Yuefeng Chen; Wei Wang 298; Xiaofeng Mao; Xiu Yan; Yue Wang; Zhigang Li 004; Wang Lu; Jindong Wang 001; Xiangyang Ji (CoRR, 2023)

Information-Containing Adversarial Perturbation for Combating Facial Manipulation Systems. - Yao Zhu 003; Yuefeng Chen; Xiaodan Li; Rong Zhang 006; Xiang Tian 002; Bolun Zheng; Yaowu Chen (IEEE Trans. Inf. Forensics Secur., 2023)

Rethinking Out-of-Distribution Detection From a Human-Centric Perspective. - Yao Zhu 003; Yuefeng Chen; Xiaodan Li; Rong Zhang 006; Hui Xue 001; Xiang Tian 002; Rongxin Jiang 001; Bolun Zheng; Yaowu Chen (Int. J. Comput. Vis., 2024)

Enhancing the Robustness of Vision-Language Foundation Models by Alignment Perturbation. - Cong Zhang; Shuhui Wang; Xiaodan Li; Yao Zhu 003; Honggang Qi; Qingming Huang (IEEE Trans. Inf. Forensics Secur., 2025)

Towards Boosting Out-of-Distribution Detection from a Spatial Feature Importance Perspective. - Yao Zhu 003; Xiu Yan; Chuanlong Xie (Int. J. Comput. Vis., 2025)

Enhancing Few-Shot CLIP With Semantic-Aware Fine-Tuning. - Yao Zhu 003; Yuefeng Chen; Xiaofeng Mao; Xiu Yan; Yue Wang; Wang Lu; Jindong Wang 001; Xiangyang Ji (IEEE Trans. Neural Networks Learn. Syst., 2025)