Advances in Multimedia Security: Ensuring Trust and Integrity in the AI Era

Title

Organizers

• Fan Qi (fanqi@email.tjut.edu.cn)
• Xiaoshan Yang (xiaoshan.yang@nlpr.ia.ac.cn)
• Zheng Wang (wangzwhu@whu.edu.cn)

Abstract

With the rapid growth of multimedia data in applications such as human-computer interaction, generative artificial intelligence, and the metaverse, security issues have become a core challenge in the digital society. This special session aims to gather innovative research in the field of multimedia security, focusing on advanced methodologies for multimedia content security, multimedia data privacy, and cross-modal generation security. Key issues to be addressed include the urgent need to develop robust watermarking, active forensics, and provenance techniques to defend against malicious attacks and deepfakes from generative AI, ensuring the authenticity and integrity of digital content. Moreover, in light of the leakage risks associated with sensitive biometric features such as faces and voiceprints, it is essential to design privacy-enhancing computational frameworks based on federated learning, striking a dynamic balance between data usability and privacy safety. Furthermore, to combat the spread of counterfeit content, there is a pressing need to develop effective detection mechanisms for identifying the misuse of multimodal generation models. We invite cutting-edge research that encompasses theoretical frameworks, algorithm innovations, and practical applications, contributing to the future development of secure multimedia systems.

Topics

• Adversarial Attacks, Defenses and Security Protection for Multimedia Content
• Digital Watermarking and Copyright Protection for Multimedia Content
• Detection and Defense Mechanisms Against Deepfakes
• Anomaly Detection in Multimedia Content
• Applications of Federated Learning in Multimedia Content
• Secure and Efficient Content Delivery in Multimedia Streaming
• Privacy-Enhancing Technologies for Multimedia Data Processing
• Abuse Detection in Multimodal Generation Models
• Ethical Considerations in Generated Multimedia Content
• Explainable Techniques for Multimedia Content Generation

Submission Site

https://cmt3.research.microsoft.com/mmasia2025

Code of Conduct