Kanghua Mo

According to our database, Kanghua Mo authored at least 9 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Security and Privacy Issues in Deep Reinforcement Learning: Threats and Countermeasures.

Kanghua Mo, Peigen Ye, Xiaojun Ren, Shaowei Wang, Wenjun Li, Jin Li

ACM Comput. Surv., June, 2024

2023

Empirical study of privacy inference attack against deep reinforcement learning models.

Huaicheng Zhou, Kanghua Mo, Teng Huang, Yongjin Li

Connect. Sci., December, 2023

Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy.

Kanghua Mo, Weixuan Tang, Jin Li, Xu Yuan

IEEE Trans. Dependable Secur. Comput., 2023

Decision Poisson: From Universal Gravitation to Offline Reinforcement Learning.

Heqiu Cai, Zhanao Zhang, Zhicong Yao, Kanghua Mo, Dixuan Chen, Hongyang Yan

Proceedings of the Artificial Intelligence Security and Privacy, 2023

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, Kanghua Mo, Wanqing Jie, Shiwen Zhang

Inf. Sci., 2022

ESM: Selfish mining under ecological footprint.

Shan Ai, Guoyu Yang, Chang Chen, Kanghua Mo, Wangyong Lv, Arthur Sandor Voundi Koe

Inf. Sci., 2022

An efficient adversarial example generation algorithm based on an accelerated gradient iterative fast gradient.

Jiabao Liu, Qixiang Zhang, Kanghua Mo, Xiaoyu Xiang, Jin Li, Debin Cheng, Rui Gao, Beishui Liu, Kongyang Chen, Guanjie Wei

Comput. Stand. Interfaces, 2022

2021

Querying little is enough: Model inversion attack via latent information.

Kanghua Mo, Xiaozhang Liu, Teng Huang, Anli Yan

Int. J. Intell. Syst., 2021

2020

Querying Little Is Enough: Model Inversion Attack via Latent Information.

Kanghua Mo, Teng Huang, Xiaoyu Xiang

Proceedings of the Machine Learning for Cyber Security - Third International Conference, 2020