Xianbo MO
Research Scientist of AI in the Faculty of Engineering at SMBU
Postdoctoral research fellow of Computer Science at Beijing Institute of Technology
莫顯博
深圳北理莫斯科大學(xué)工程系研究員
北京理工大學(xué)計(jì)算機(jī)學(xué)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.
莫顯博博士分別于2019年和2024年，畢業(yè)于深圳大學(xué)（計(jì)算機(jī)科學(xué)與技術(shù)專(zhuān)業(yè)，學(xué)士）、深圳大學(xué)（信息與通信工程專(zhuān)業(yè)，博士）。他現(xiàn)在是北京理工大學(xué)和深圳北理莫斯科大學(xué)聯(lián)合培養(yǎng)的博士后。研究興趣包括多媒體取證與安全、信息隱藏、深度強(qiáng)化學(xué)習(xí)。
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.
