Dr. BIN LI received the B.E. degree in communication engineering and the Ph.D. degree in communication and information system from Sun Yat-sen University, Guangzhou, China, in 2004 and 2009, respectively. He was a Visiting Scholar with the New Jersey Institute of Technology, Newark, NJ, USA, from 2007 to 2008. In 2009, he joined Shenzhen University, Shenzhen, China, where he is currently a professor. He is the Director of Guangdong Key Laboratory of Intelligent Information Processing and the Director of Shenzhen Key Laboratory of Media Security. His current research interests include image processing, multimedia forensics, and pattern recognition.

[1]2004-2009, PhD., Sun Yat-sen University

[2]2007-2008, Visiting Scholar, New Jersey Institute of Technology

[1]2009-Now, Shenzhen University

[1]Key Technologies for Image Steganography Security. Second Prize in Natural Science, Guangdong Computer Society Science and Technology Award. February 2023.

[2]Theory and Methods of Information Hiding. First Prize in Natural Science Award, CCF Science and Technology Award. October 2019.

[3]Theory and Methods of Adaptive Information Steganography Security. Second Prize in Natural Science Award, Shenzhen. May 2022.

[4]Kengtang Zheng, Bin Li*, Jinhua Zeng. Document Image Tampering and Desensitization Localization Using Attention Mechanism. Excellent Paper Award at the Third CSIG Media Forensics and Security Conference. November 2022.

[5]Supervisor of One Hundred Excellent Master's Theses. July 2023.

[6]Tencent Excellent Teaching Management Team Award. June 2023.

[1]Multimedia Forensics and Security

[2]Information Hiding

[3]Image/Audio/Video Signal Processing

[4]Adversarial Machine Learning

[1]B. Li, J. Chen, Y. Xu, W. Li, and Z. Liu, "DRAW: Dual-Decoder-Based Robust Audio Watermarking Against Desynchronization and Replay Attacks," IEEE Transactions on Information Forensics and Security, vol. 19, pp. 6529–6544, 2024.

[2]D. Lin, B. Tondi, B. Li*, and M. Barni, "A CycleGAN Watermarking Method for Ownership Verification," IEEE Transactions on Dependable and Secure Computing, pp. 1–15, 2024.

[3]W. Li, B. Li*, W. Zhang, and S. Zhang, "Quaternary Quantized Gaussian Modulation with Optimal Polarity Map Selection for JPEG Steganography," IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5026–5040, 2023.

[4]W. Li, S. Wu, B. Li*, W. Tang, and X. Zhang, "Payload-Independent Direct Cost Learning for Image Steganography," IEEE Transactions on Circuits and Systems for Video Technology.

[5]S. Tan, Q. Li, L. Li, B. Li, and J. Huang, "STD-NET: Search of Image Steganalytic Deep-Learning Architecture via Hierarchical Tensor Decomposition," IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 3, pp. 2657–2673, May-June 2023.

[6]W. Tang, B. Li*, W. Li, Y. Wang, and J. Huang, "Reinforcement Learning of Non-additive Joint Steganographic Embedding Costs with Attention Mechanism," Science China-Information Sciences, vol. 66, no. 132305, pp. 1–14, Mar. 2023.

[7]H. Chen, Y. Li, D. Lin, B. Li*, and J. Wu, "Watching the BiG Artifacts: Exposing DeepFake Videos via Bi-Granularity Artifacts," Pattern Recognition, vol. 135, no. 109179, pp. 1–13, Mar. 2023.

[8]H. Chen, Y. Lin, B. Li*, and S. Tan, "Learning Features of Intra-consistency and Inter-diversity: Keys towards Generalizable Deepfake Detection," IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 3, pp. 1468–1480, Mar. 2023.

[9]G. Li, B. Li*, S. Tan, and G. Qiu, "Learning Deep Co-occurrence Features," IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 4, pp. 1610–1623, Apr. 2023.

[10]T. Chen, B. Li*, and J. Zeng, "Learning Traces by Yourself: Blind Image Forgery Localization via Anomaly Detection With ViT-VAE," IEEE Signal Processing Letters, vol. 30, pp. 150–154, Feb. 2023.

[11]W. Tang, B. Li*, M. Barni, J. Li, and J. Huang, "Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge," IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 6, pp. 4081–4095, Jun. 2022.

[12]X. Qin, B. Li*, S. Tan, W. Tang, and J. Huang, "Gradually Enhanced Adversarial Perturbations on Color Pixel Vectors for Image Steganography," IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 8, pp. 5110–5123, Aug. 2022.

[13]L. Zhuo, S. Tan, B. Li, and J. Huang, "Self-Adversarial Training Incorporating Forgery Attention for Image Forgery Localization," IEEE Transactions on Information Forensics and Security, vol. 17, pp. 819–834, Feb. 2022.

[14]Q. Li, S. Chen, S. Tan, B. Li, and J. Huang, "One-Class Double Compression Detection of Advanced Videos Based on Simple Gaussian Distribution Model," IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 4, pp. 2496–2500, Apr. 2022.

[15]W. Tang, B. Li*, M. Barni, J. Li, and J. Huang, "An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning," IEEE Transactions on Information Forensics and Security, vol. 16, pp. 952–967, Sep. 2021.

[1]Research on Steganalysis Networks for Cross-Domain Few-Shot Learning, NSFC.

[2]Research on JPEG Image Forensics and Anti-Forensics Based on Deep Learning in Adversarial Environments, NSFC.

[3]Research on Non-Additive Distortion Image Steganography, NSFC.

[4]Research on Forensic Technology for AI-Generated Fake Audiovisual Media, Guangdong NSF

[5]JPEG Image Tampering Detection Based on Quantization Noise and Deep Architecture, Guangdong NSF