Shunquan Tan, Ph.D.
Professor, Faculty of Engineering, Shenzhen MSU-BIT University
IEEE Senior Member | Deputy Director, Shenzhen Key Laboratory of Media Security
Research Interests
Multimedia Information Security, Steganography and Steganalysis, Digital Media Forensics, Deep Machine Learning, Pattern Recognition, AI Security, IoT Security
Education
- Ph.D. in Computer Science, Sun Yat-sen University, China, July 2007
- B.S. in Computational Mathematics, Sun Yat-sen University, China, July 2002
Research and Academic Experience
- Professor, Faculty of Engineering, Shenzhen MSU-BIT University, 2025–Present
- Professor, College of Computer Science & Software Engineering, Shenzhen University, 2024
- Associate Professor and Research Fellow, College of Computer Science & Software Engineering, Shenzhen University, 2018–2023
- Lecturer, College of Computer Science & Software Engineering, Shenzhen University, 2007–2017
- Visiting Scholar, New Jersey Institute of Technology, NJ, USA, 2005–2006
Academic Services
- Associate Editor, EURASIP Journal on Information Security
- Member, IEEE Information Forensics and Security Technical Committee (2022–2024)
- Member, Digital Media Forensics and Security Professional Committee, China Society of Image and Graphics
Research Grants
- Principal Investigator, Research on light-weight and robust image tampering detection and localization models in real-world environment, National Natural Science Funds of China (Grant No. 62272314), 540,000 RMB
- Principal Investigator, Research on tensor-decompression-framework based deep learning information-hiding warfare, National Natural Science Funds of China (Grant No. 61772349), 590,000 RMB
- Principal Investigator, Applications of Large-scaled Distributed Deep Learning Framework in Steganalysis, National Natural Science Funds of China (Grant No. 61402295), 260,000 RMB
- Principal Investigator, Steganalysis of Contents-adaptive Steganography based on B-spline Fitting, Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (Grant No. 2012LYM_0117), 30,000 RMB
- Principal Investigator, Steganalysis based on Large-scaled Distributed Deep Learning Framework, Fundamental Research Program of Shenzhen City, China (Grant No. JCYJ20140418182819173), 230,000 RMB
Selected Publications
Journal Papers
- I. Hussain, S. Tan, J. Huang, “Few-shot Based Learning Recaptured Image Detection with Multi-Scale Feature Fusion and Attention,” *Pattern Recognition, 2025, 161: 111248
- I. Hussain, S. Tan, J. Huang, “A Semi-Supervised Deep Learning Approach for Cropped Image Detection,” *Expert Systems with Applications, 2024, 243: 122832
- R. Peng, S. Tan, et al., “Employing Reinforcement Learning to Construct a Decision-Making Environment For Image Forgery Localization,” *IEEE Transactions on Information Forensics and Security, 2024, 19: 4820–4834
- X. Mo, S. Tan, et al., “ReLOAD: Using Reinforcement Learning to Optimize Asymmetric Distortion for Additive Steganography,” *IEEE Transactions on Information Forensics and Security, 2023, 18: 1524–1538
- K. Wei, W. Luo, S. Tan, J. Huang, “Universal Deep Network for Steganalysis of Color Image Based on Channel Representation,” IEEE Transactions on Information Forensics and Security, 2022, 17: 3022–3036
Conference Papers
- X. Mo, S. Tan, B. Li, J. Huang, “Query-efficient Attack for Black-box Image Inpainting Forensics via Reinforcement Learning,” *AAAI Conference on Artificial Intelligence, 2025
- X. Mo, S. Tan, et al., “Poster: Query-efficient black-box attack for image forgery localization via reinforcement learning,” *ACM SIGSAC Conference on Computer and Communications Security (CCS), 2023, 3552–3554
- G. Li, B. Li, C. Chen, S. Tan, G. Qiu, “Learning General Gaussian Mixture Model with Integral Cosine Similarity,” International Joint Conference on Artificial Intelligence (IJCAI), 2022, 3201–3207
Full publication list on Google Scholar