Advancing Example Exploitation Can Alleviate Critical Challenges in Adversarial Training. - Yao Ge 004; Yun Li 009; Keji Han; Junyi Zhu 006; Xianzhong Long (ICCV, 2023)

Attribution of Adversarial Attacks via Multi-task Learning. - Zhongyi Guo; Keji Han; Yao Ge 004; Yun Li 009; Wei Ji (ICONIP (2), 2023)

Scalable Attribution of Adversarial Attacks via Multi-Task Learning. - Zhongyi Guo; Keji Han; Yao Ge 004; Wei Ji; Yun Li 009 (CoRR, 2023)

Rethinking the validity of perturbation in single-step adversarial training. - Yao Ge 004; Yun Li 009; Keji Han (Pattern Recognit., 2025)

DLR: Adversarial examples detection and label recovery for deep neural networks. - Keji Han; Yao Ge 004; Ruchuan Wang; Yun Li 009 (Pattern Recognit. Lett., 2025)