Querying Little Is Enough: Model Inversion Attack via Latent Information. - Kanghua Mo; Teng Huang; Xiaoyu Xiang (ML4CS (2), 2020)

Querying little is enough: Model inversion attack via latent information. - Kanghua Mo; Xiaozhang Liu; Teng Huang 001; Anli Yan (Int. J. Intell. Syst., 2021)

An efficient adversarial example generation algorithm based on an accelerated gradient iterative fast gradient. - Jiabao Liu; Qixiang Zhang; Kanghua Mo; Xiaoyu Xiang; Jin Li 002; Debin Cheng; Rui Gao; Beishui Liu; Kongyang Chen; Guanjie Wei (Comput. Stand. Interfaces, 2022)

ESM: Selfish mining under ecological footprint. - Shan Ai; Guoyu Yang; Chang Chen; Kanghua Mo; Wangyong Lv; Arthur Sandor Voundi Koe (Inf. Sci., 2022)

Sender anonymity: Applying ring signature in gateway-based blockchain for IoT is not enough. - Arthur Sandor Voundi Koe; Shan Ai; Pengfei Huang; Anli Yan; Juan Tang; Qi Chen 024; Kanghua Mo; Wanqing Jie; Shiwen Zhang 004 (Inf. Sci., 2022)

Decision Poisson: From Universal Gravitation to Offline Reinforcement Learning. - Heqiu Cai; Zhanao Zhang; Zhicong Yao; Kanghua Mo; Dixuan Chen; Hongyang Yan (AIS&P (1), 2023)

Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy. - Kanghua Mo; Weixuan Tang; Jin Li 002; Xu Yuan 001 (IEEE Trans. Dependable Secur. Comput., 2023)

Empirical study of privacy inference attack against deep reinforcement learning models. - Huaicheng Zhou; Kanghua Mo; Teng Huang; Yongjin Li (Connect. Sci., 2023)

Exploring the vulnerability of self-supervised monocular depth estimation models. - Ruitao Hou; Kanghua Mo; Yucheng Long; Ning Li; Yuan Rao 002 (Inf. Sci., 2024)

Security and Privacy Issues in Deep Reinforcement Learning: Threats and Countermeasures. - Kanghua Mo; Peigen Ye; Xiaojun Ren; Shaowei Wang 003; Wenjun Li; Jin Li 002 (ACM Comput. Surv., 2024)

Distraction is All You Need for Multimodal Large Language Model Jailbreaking. - Zuopeng Yang; Jiluan Fan; Anli Yan; Erdun Gao; Xin Lin; Tao Li; Kanghua Mo; Changyu Dong (CVPR, 2025)

Inverse correction-optimized vertical federated unlearning. - Lang Li; Li Hu; Kanghua Mo; Ziyu Ding; Yaqi Wu; Hongyang Yan; Jin Li 002 (J. Supercomput., 2025)

Attractive Metadata Attack: Inducing LLM Agents to Invoke Malicious Tools. - Kanghua Mo; Li Hu; Yucheng Long; Zhihao Li (CoRR, 2025)