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博士后

SMBU

作者:    审核:    发布时间:2023-09-14    阅读次数:

Xianbo MO

Research Scientist of AI in the Faculty of Engineering at SMBU

Postdoctoral research fellow of Computer Science at Beijing Institute of Technology

莫显博

深圳北理莫斯科大学工程系研究员

北京理工大学计算机学院博士后


Dr. Xianbo Mo received the B.S. degree in computer science and technology from Shenzhen University, Shenzhen, China, in 2019 and the Ph.D. degree in information and communication engineering from Shenzhen University, Shenzhen, China, in 2024. He is currently working as post-doctoral at Beijing Institute of Technology(BIT) and  Shenzhen MSU-BIT University (SMBU). His research interests include multimedia forensics and security, information hiding, and deep reinforcement learning.

莫显博博士分别于2019年和2024年,毕业于深圳大学(计算机科学与技术专业,学士)、深圳大学(信息与通信工程专业,博士)。他现在是北京理工大学和深圳北理莫斯科大学联合培养的博士后。研究兴趣包括多媒体取证与安全、信息隐藏、深度强化学习。

Selected publications

1. Xianbo Mo, Shunquan Tan, Bin Li, Jiwu Huang,“MCTSteg: A Monte Carlo Tree Search-Based Reinforcement Learning Framework for Universal Non-Additive Steganography, IEEE Trans. on Information Forensics & Security., vol. 16, pp. 4306-4320, 2021

2. Xianbo Mo, Shunquan Tan, Weixuan Tang, Bin Li, Jiwu Huang, “ReLOAD: using reinforcement learning to optimize asymmetric distortion for additive steganography,” IEEE Trans. on Information Forensics & Security. vol. 18, 1524-1538, 2023

3. Xianbo Mo, Shunquan Tan, Bin Li, Jiwu Huang, “Poster: Query-efficient Black-box Attack for Image Forgery Localization via Reinforcement Learning,” Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. 2023.

4. Rongxuan Peng, Xianbo Mo, Shunquan Tan, Bin Li, Jiwu Huang, A Keyless Extraction Framework Targeting at Deep Learning Based Image-Within-Image Models, IEEE International Conference on Acoustics, Speech and Signal Processing. 2024.

5. Rongxuan Peng, Xianbo Mo, Shunquan Tan, Bin Li, Jiwu Huang, Employing Reinforcement Learning to Construct a Decision-Making Environment for Image Forgery Localization, in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 4820-4834, 2024.




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