Publications

You can also find my articles on my Google Scholar profile.

JULI: Jailbreak Large Language Models by Self-Introspection

Jesson Wang, Zhanhao Hu, David Wagner

International Conference on Learning Representations (ICLR), 2026

We propose Jailbreaking Using LLM Introspection (JULI), which jailbreaks LLMs by manipulating the token log probabilities, using a tiny plug-in block, BiasNet.

Recommended citation: Jesson Wang, Zhanhao Hu, David Wagner. (2026). "JULI: Jailbreak Large Language Models by Self-Introspection." International Conference on Learning Representations (ICLR). https://arxiv.org/pdf/2505.11790

MobHAR: Source-free Knowledge Transfer for Human Activity Recognition on Mobile Devices

Meng Xue, Yinan Zhu, Wentao Xie, Zhixian Wang, Yanjiao Chen, Kui Jiang, Qian Zhang

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2025

This paper presents a novel source-free domain adaptation framework for HAR that effectively handles substantial domain discrepancies across different datasets.

Recommended citation: Meng Xue, Yinan Zhu, Wentao Xie, Zhixian Wang, Yanjiao Chen, Kui Jiang, Qian Zhang. (2025). "MobHAR: Source-free Knowledge Transfer for Human Activity Recognition on Mobile Devices." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 9(1). https://dl.acm.org/doi/10.1145/3712620

ARTEMIS: Defending Against Backdoor Attacks via Distribution Shift

Meng Xue, Zhixian Wang, Qian Zhang, Xueluan Gong, Zhihang Liu, Yanjiao Chen

IEEE Transactions on Dependable and Secure Computing (TDSC), 2025

In this work, we propose a novel backdoor defense approach called ARTEMIS, which utilizes distribution shifts to eliminate the discrepancy between poisoned and benign samples in the feature space.

Recommended citation: Meng Xue, Zhixian Wang, Qian Zhang, Xueluan Gong, Zhihang Liu, Yanjiao Chen. (2025). "ARTEMIS: Defending Against Backdoor Attacks via Distribution Shift." IEEE Transactions on Dependable and Secure Computing. 22(2). https://ieeexplore.ieee.org/abstract/document/10702439