arXiv Computer Vision
6d ago

Generalized Design Choices for Deepfake Detectors

The paper examines how implementation details—such as data preprocessing, augmentation, and optimization—affect deepfake detector performance more than core architectural choices. By systematically isolating these factors across training, inference, and incremental updates, the authors identify design choices that consistently improve accuracy and generalization. These findings provide architecture‑agnostic best practices that enable state‑of‑the‑art performance on the AI‑GenBench benchmark.

By Lorenzo Pellegrini, Serafino Pandolfini, Davide Maltoni, Matteo Ferrara, Marco Prati, Marco Ramilli
arXiv AI
Jun 29

Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook

arXiv:2411. 19537v2 Announce Type: replace-cross Abstract: We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content.

By Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru, Nicolae Catalin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah