DFF-2025 Workshop, ACM Multimedia 2025
DFF-2025 Workshop, ACM Multimedia 2025
Workshop – Overview
The rapid advancements in deep learning, particularly in generative models such as Generative Adversarial Networks (GANs) and Diffusion Models (DM), have significantly improved the quality and realism of synthetic media, commonly referred to as deepfakes. While these technologies unlock creative possibilities, they simultaneously raise critical concerns regarding digital content authenticity. Deepfake generation and detection are now at the core of multimedia forensics, requiring robust and generalizable methods to identify manipulated content effectively. This workshop aims to bring together researchers and practitioners from diverse fields, including computer vision, multimedia forensics and adversarial machine learning, to explore emerging challenges and solutions in deepfake detection, attribution, recognition and counter-forensic strategies. Specifically, it will address the limitations of current detection models in generalizing to real-world scenarios, the interpretability of forensic results, and the risks posed by synthetic content. Additionally, the workshop will promote discussions on dataset biases, multimodal deepfake analysis, the forensic ballistics of synthetic media, and the legal and ethical implications of deepfake technology, including regulatory challenges and forensic admissibility in court. Through keynote presentations, research paper presentations, and panel discussions, the workshop will provide a comprehensive overview of state-of-the-art deepfake research while promoting interdisciplinary collaborations to address this pressing societal issue.
Topics of interest in this Special Issue include but are not limited to:
· Deepfake Detection on Images, Video, and Audio
· Multimodal Deepfake Detection
· Deepfake Model Recognition and Attribution
· Forensic Ballistics for Deepfake Analysis
· Adversarial Forensics and Counter-Forensic Techniques
· Generative Models for Deepfake Creation
· Multimodal Datasets for Deepfake Detection and Generation
· Explainability and Interpretability in AI Forensics
· Passive Deepfake Authentication Methods
· Active Deepfake Authentication Methods
· Legal and Ethical Implications of Deepfakes: Detection, Regulation and Accountability
Organizers: Luca Guarnera, Francesco Guarnera, Sebastiano Battiato, Giovanni Puglisi, Zahid Akhtar, Mirko Casu, Orazio Pontorno, Claudio Ragaglia
#acmmultimedia #IPLab #Deepfake
Organizers: Luca Guarnera, Francesco Guarnera, Sebastiano Battiato, Giovanni Puglisi, Zahid Akhtar, Mirko Casu, Orazio Pontorno, Claudio Ragaglia
#acmmultimedia #IPLab #Deepfake