Weisi Lin is an active researcher and research leader in image processing, perception-based signal modelling and assessment, video compression, and multimedia communication. He had been the Lab Head, Visual Processing, Institute for Infocomm Research (I2R), Singapore. He is currently a President’s Chair Professor in College of Computing and Data Science, Nanyang Technological University (NTU), Singapore, where he also serves as the Associate Dean (Research). He is a Fellow of IEEE and IET. He has been awarded Highly Cited Researcher since 2019 by Clarivate Analytics, and elected for the Research Award 2023, College of Engineering, NTU. He has been a Distinguished Lecturer in both IEEE Circuits and Systems Society (2016-17) and Asia-Pacific Signal and Information Processing Association (2012-13). He has been an Associate Editor for IEEE Trans. Neural Networks Learn. Syst., IEEE Trans. Image Process., IEEE Trans. Circuits Syst. Video Technol., IEEE Trans. Multim., IEEE Sig. Process. Lett., Quality and User Experience, and J. Visual Commun. Image Represent. He serves as a General Co-Chair for IEEE ICME 2025 and the Lead General Chair for IEEE ICIP 2027, and has been a TP Chair for several international conferences. He believes that good theory is practical and has delivered 10+ major systems for industrial deployment with the technology developed. He has been the Programme Lead for the Temasek Foundation Programme for AI Research, Education & Innovation in Asia, 2020-2024.

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Professor KWONG Sam Tak Wu is the Associate Vice-President (Strategic Research), J.K. Lee Chair Professor of Computational Intelligence, the Dean of the School of Graduate Studies and the Acting Dean of the School of Data Science of Lingnan University. Professor Kwong is a distinguished scholar in evolutionary computation, artificial intelligence (AI) solutions, and image/video processing, with a strong record of scientific innovations and real-world impacts. Professor Kwong was listed as the World’s Top 2% Scientists by Stanford University since 2021 and one of the most highly cited researchers by Clarivate in 2022 and 2023. He has also been actively engaged in knowledge transfer between academia and industry. He was elevated to IEEE Fellow in 2014 for his contributions to optimization techniques in cybernetics and video coding. He was a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) in 2022, and the President of the IEEE Systems, Man, and Cybernetics Society (SMCS) in 2021-23. He is a fellow of US National Academy of Inventors (NAI) and the Hong Kong Academy Awards of Engineering and Sciences (HKAES). Professor Kwong has a prolific publication record with over 350 journal articles, and 160 conference papers with an h-index of 84 based on Google Scholar. He is currently the associate editor of a number of leading IEEE transaction journals.

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Benedetta Tondi received the MSc degree in electronics and communications engineering, and the PhD degree in information engineering and mathematical sciences, from the University of Siena, Italy, in 2012 and 2016, respectively. She is currently an Associate Professor at the Department of Information Engineering and Mathematics, University of Siena. She is a member of the Information Forensics and Security Technical Committee of the IEEE Signal Processing Society. Her research interests focus on adversarial signal processing, multimedia forensics, AI security, and watermarking of deep neural networks. She currently serves as an Associate Editor for the IEEE Transactions on Information Forensics and Security and the IEEE Signal Processing Letters. She has been Technical Program Chair of ACM IH&MMSEC 2022 and Area Chair of several IEEE conferences and workshops. She has received Best Paper Awards at WIFS and MMEDIA. She is the recipient of the 2017 GTTI PhD Award for the best PhD thesis defended at an Italian University in the areas of Communications Technologies (Signal Processing, Digital Communications, Networking).

Speech Abstract: As generative AI continues to evolve, concerns around transparency, authenticity, and intellectual property are becoming more pressing. Tools like GPT-4, DALL·E, and Midjourney, powered by large-scale foundation models, are now widely used to generate realistic images, text, and other media. However, their ability to produce synthetic content indistinguishable from human-generated material raises critical issues—from copyright infringement to the spread of misinformation and deepfakes.
In response to these challenges, model watermarking has emerged as a promising approach to protect the intellectual property of generative AI models and to promote trustworthy AI. By modifying the AI models in such a way that imperceptible signals are embedded into AI-generated outputs, watermarking enables downstream identification, authentication, and traceability of synthetic media.
After a general introduction on the topic of Deep Neural Network (DNN) watermarking, the talk delves into technical strategies for embedding robust watermarks into GANs and Diffusion Models—two of the most powerful families of generative models—in such a way that each model output contains an embedded watermark that can be reliably recovered during the identification phase. These watermarks enable copyright protection and provide a means to trace generated content back to the source model that produced it, supporting model attribution and content authentication. Methods embedding watermarks post-training are also discussed. The keynote aims to offer both a conceptual and practical understanding of watermarking as a cornerstone for building trust in generative AI, while also outlining open challenges and limitations.