Xianbo Mo

Tenure-Track Associate Professor, Faculty of Engineering, Shenzhen MSU-BIT University

莫显博

深圳北理莫斯科大学预聘副教授

Xianbo Mo is a member of the Technical Committee on Digital Media Forensics and Security of the China Society of Image and Graphics, and a member of the Technical Committee on Multimedia Security of the Chinese Society for Cyberspace Security. He received his B.Sc. degree in Computer Science and Technology from Shenzhen University in 2019 and his Ph.D. degree in Information and Communication Engineering from Shenzhen University in 2024. His doctoral dissertation was selected as one of the Shenzhen University’s Outstanding Doctoral Dissertations. From 2024 to 2026, he conducted postdoctoral research at the School of Computer Science and Technology, Beijing Institute of Technology.

His research focused on digital image content security and artificial intelligence security. In recent years, he has published multiple papers in leading journals and conferences in the field, including IEEE Transactions on Information Forensics and Security (IEEE TIFS), AAAI, and ACM CCS. He has also filed multiple Chinese invention patent applications and obtained several software copyrights. He currently serves as the principal investigator of a Young Scientists Fund (Category C) project supported by the National Natural Science Foundation of China and has participated in several General Program projects funded by the Foundation.

莫显博，中国图象图形学学会数字媒体取证与安全专业委员会委员，中国网络空间安全学会多媒体安全专业委员会会员。2019年获深圳大学计算机科学与技术专业学士学位，2024年获深圳大学信息与通信工程专业博士学位，博士论文获评深圳大学十佳博士学位论文，2024年—2026年于北京理工大学计算机学院开展博士后研究。长期从事数字图像内容安全研究，人工智能安全研究。近年来，在IEEE TIFS、AAAI、ACM CCS等本领域国内外权威学术期刊和会议发表学术论文多篇，申请中国发明专利和软件著作权等多项。现主持国家自然科学基金青年C类项目，参与多项国家自然科学基金面上项目。

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] Xianbo Mo, Shunquan Tan, Bin Li, Jiwu Huang, “Query-efficient attack for black-box image inpainting forensics via reinforcement learning”, Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(18): 19503-19511.

[5] Xianbo Mo, Shunquan Tan, Rongxuan Peng, Bin Li, Jiwu Huang, "Query-Efficient Hard-Label Attacks Against Black-Box Image Forgery Localization Model via Reinforcement Learning," in IEEE Transactions on Information Forensics and Security, vol. 21, pp. 4578-4593, 2026.