Privacy-Preserving Low-Rank Adaptation Against Membership Inference Attacks for Latent Diffusion Models. - Luo, Zihao; Xu, Xilie; Liu, Feng; Koh, Yun Sing; Wang, Di; Zhang, Jingfeng (2025)

An LLM can Fool Itself: A Prompt-Based Adversarial Attack. - Xu, Xilie; Kong, Keyi; Liu, Ning; Cui, Lizhen; Wang, Di; Zhang, Jingfeng; Kankanhalli, Mohan S. (2024)

AutoLoRa: An Automated Robust Fine-Tuning Framework. - Xu, Xilie; Zhang, Jingfeng; Kankanhalli, Mohan S. (2024)

Perplexity-aware Correction for Robust Alignment with Noisy Preferences. - Kong, Keyi; Xu, Xilie; Wang, Di; Zhang, Jingfeng; Kankanhalli, Mohan S. (2024)

Privacy-Preserving Low-Rank Adaptation for Latent Diffusion Models. - Luo, Zihao; Xu, Xilie; Liu, Feng; Koh, Yun Sing; Wang, Di; Zhang, Jingfeng (CoRR, 2024)

Technical Report for ICML 2024 TiFA Workshop MLLM Attack Challenge: Suffix Injection and Projected Gradient Descent Can Easily Fool An MLLM. - Guo, Yangyang; Xu, Ziwei; Xu, Xilie; Wong, Yongkang; Nie, Liqiang; Kankanhalli, Mohan S. (CoRR, 2024)

Decision Boundary-Aware Data Augmentation for Adversarial Training. - Chen, Chen; Zhang, Jingfeng; Xu, Xilie; Lyu, Lingjuan; Chen, Chaochao; Hu, Tianlei; Chen, Gang (IEEE Trans. Dependable Secur. Comput., 2023)

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. - Xu, Xilie; Zhang, Jingfeng; Liu, Feng; Sugiyama, Masashi; Kankanhalli, Mohan S. (2023)

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. - Xu, Xilie; Zhang, Jingfeng; Liu, Feng; Sugiyama, Masashi; Kankanhalli, Mohan S. (2023)

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. - Xu, Xilie; Zhang, Jingfeng; Liu, Feng; Sugiyama, Masashi; Kankanhalli, Mohan S. (CoRR, 2023)

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. - Xu, Xilie; Zhang, Jingfeng; Liu, Feng; Sugiyama, Masashi; Kankanhalli, Mohan S. (CoRR, 2023)

AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework. - Xu, Xilie; Zhang, Jingfeng; Kankanhalli, Mohan S. (CoRR, 2023)

An LLM can Fool Itself: A Prompt-Based Adversarial Attack. - Xu, Xilie; Kong, Keyi; Liu, Ning; Cui, Lizhen; Wang, Di; Zhang, Jingfeng; Kankanhalli, Mohan S. (CoRR, 2023)

NoiLin: Improving adversarial training and correcting stereotype of noisy labels. - Zhang, Jingfeng; Xu, Xilie; Han, Bo; Liu, Tongliang; Cui, Lizhen; Niu, Gang; Sugiyama, Masashi (Trans. Mach. Learn. Res., 2022)

Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. - Xu, Xilie; Zhang, Jingfeng; Liu, Feng; Sugiyama, Masashi; Kankanhalli, Mohan S. (2022)

Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests. - Xu, Xilie; Zhang, Jingfeng; Liu, Feng; Sugiyama, Masashi; Kankanhalli, Mohan S. (CoRR, 2022)

Guided Interpolation for Adversarial Training. - Chen, Chen; Zhang, Jingfeng; Xu, Xilie; Hu, Tianlei; Niu, Gang; Chen, Gang; Sugiyama, Masashi (CoRR, 2021)

NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? - Zhang, Jingfeng; Xu, Xilie; Han, Bo; Liu, Tongliang; Niu, Gang; Cui, Lizhen; Sugiyama, Masashi (CoRR, 2021)

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. - Zhang, Jingfeng; Xu, Xilie; Han, Bo; Niu, Gang; Cui, Lizhen; Sugiyama, Masashi; Kankanhalli, Mohan S. (2020)

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. - Zhang, Jingfeng; Xu, Xilie; Han, Bo; Niu, Gang; Cui, Lizhen; Sugiyama, Masashi; Kankanhalli, Mohan S. (CoRR, 2020)