VERIMEDIA

Call for Papers

The rapid growth of generative multimodal processing and dissemination technologies has enabled the creation of an unprecedented amount of realistic synthetic media. Research on synthetic media detection has produced methods that perform well on controlled benchmarks, but whose behavior degrades once they are applied to data that has undergone social-media re-encoding, originates from heterogeneous or previously unseen sources, or is subject to deliberate adversarial manipulation.

Therefore, this workshop concentrates on the properties that decide whether a forensic method is usable in deployment rather than only in evaluation: robustness to distribution shift and real-world degradation, generalization to out-of-distribution data, resistance to adversarial perturbations, computational efficiency under resource constraints, interpretability of decisions, and consistency across modalities and sources. Particular focus will be on the deployment of autonomous AI agents for media verification, as well as lifelong media authentication, a forward-looking perspective on AI's role in safeguarding media integrity.

This workshop is aimed at researchers from various fields, including computational intelligence (e.g., deep learning, machine learning), artificial intelligence (e.g., natural language processing, knowledge modeling, computer vision), social network analysis, and signal processing who are interested in applying their methods and techniques to the challenges of detecting and countering misinformation. Additionally, professionals, practitioners, and stakeholders active in fields like journalism, communication, fact-checking, and law enforcement will also find the discussions and outcomes of this workshop highly relevant.

We invite submissions on topics including, but not limited to:

• Multimedia Forensics: Detection and attribution of manipulated content across various media types (images, video, text, and audio).
• Deepfake Detection: Innovations in identifying AI-generated content.
• Adversarial Machine Learning: Challenges and solutions in combating adversarial threats.
• AI-Generated Media Attribution: Techniques for tracing and authenticating content origin.
• Autonomous AI Agents for Media Verification: Agent-based approaches to content authentication and fact-checking.
• Lifelong Media Authentication: Long-term strategies for maintaining media integrity.
• Robustness and Generalization: Detection under distribution shift, social-media re-encoding, real-world degradation, and out-of-distribution data.
• Evaluation Protocols and Benchmarks: Methodologies that reflect real deployment conditions.
• Explainability and Trustworthiness: Interpretable and reliable forensic decisions at scale.
• Applications in Disinformation Detection: Case studies in forensics, fake news debunking, infodemic control, and politics.