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ORCIDDFF@ACM Multimedia 2025: Dublin, Ireland
- Luca Guarnera, Francesco Guarnera, Sebastiano Battiato, Giovanni Puglisi, Zahid Akhtar:
Proceedings of the 1st on Deepfake Forensics Workshop: Detection, Attribution, Recognition, and Adversarial Challenges in the Era of AI-Generated Media, DFF 2025, Dublin, Ireland, October 27-28, 2025. ACM 2025, ISBN 979-8-4007-2047-5 - Gian Luca Marcialis:
Facial Deepfake Detection: Five Years of Research Efforts and the FF4ALL-SERICS Experience. 1-2 - Zhipeng Yuan, Kai Wang, Weize Quan, Dong-Ming Yan, Tieru Wu:
CLIP-Flow: A Universal Discriminator for AI-Generated Images Inspired by Anomaly Detection. 3-11 - Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene, Abdelmalik Taleb-Ahmed, Abdenour Hadid:
REVEAL: A Retrieval-Augmented Generation Approach for Contextual Identification of Synthetic Visual Content. 12-20 - Claudio Vittorio Ragaglia, Lorenzo Catania, Francesco Guarnera, Dario Allegra, Sebastiano Battiato:
What if Retrieval Could Work Before Decoding? The case of JPEG AI Latents for Deepfake Source Attribution. 21-28 - Andrea Montibeller, Dasara Shullani, Daniele Baracchi, Alessandro Piva, Giulia Boato:
Bridging the Gap: A Framework for Real-World Video Deepfake Detection via Social Network Compression Emulation. 29-36 - Giuseppe Mazzola, Liliana Lo Presti, Marco La Cascia:
How Well Do Simple Features Detect Fake Faces? A Comparison with Deep Learning. 37-44 - Chenyang Zhu, Xing Zhang, Yuyang Sun, Ching-Chun Chang, Isao Echizen:
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era. 45-54 - Marco Huber, Anh Thi Luu, Naser Damer:
Morphing Resilient Face Recognition by Informed Frequency Selection. 55-64 - Alan Perotti, Marco Nurisso, Mirko Zaffaroni:
No Detector to Rule Them All. 65-72 - Zhi Cao, Zhongyuan Wang, Run Wang, Yuhong Yang, Feng Tian, Gang Wu, Atsushi Suzuki:
MGGA: Universal Perturbations against Deepfake via Multiple Model-based Gradient-Guided Feature Layer Attack. 73-82 - Abhinav Dhall:
Multimodal Deepfake Detection Across Cultures and Languages. 83 - Taiba Majid Wani, Irene Amerini:
Learning to Fuse: A Gated Multi-Stream Framework for Generalized Audio Deepfake Detection. 84-92 - Utkarsh Venaik, Akash Kushwaha, Nabeel Koya A, Rajiv Ratn Shah:
SASDN: A Generalizable and Minimal-Intervention LLM-Integrated Framework for Continual Adaptation in Spoofed Speech Detection. 93-100 - Andrea Di Pierno, Luca Guarnera, Dario Allegra, Sebastiano Battiato:
Towards Reliable Audio Deepfake Attribution and Model Recognition: A Multi-Level Autoencoder-Based Framework. 101-109 - Jinyu Wang, Xin Jin, Huaye Wang, Longteng Jiang:
VAD-Lip: Visual and Audio Deepfake Detection via Lip Features. 110-117 - Umur Aybars Ciftci, Nicholas Solar, Emily Greene, Anthony Rhodes, Ilke Demir:
Adversarial Reality for Frame-based Deepfake Detectors. 118-127 - Qiushi Li, Stefano Berretti, Roberto Caldelli:
Visual Quality Improved Watermarking based on Dual-Reference Loss for Deepfake Attribution. 128-134 - Chenfeng Du, Yuchen Li, Heng Huang, Xin Jin, Xianglong Zeng:
Multimodal Large Models for Image Tampering Detection and Explanation: From Detection to Reasoning. 135-141
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