[2508.21048] Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning

Computer Science > Computer Vision and Pattern Recognition arXiv:2508.21048 (cs) [Submitted on 28 Aug 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning Authors:Hao Tan, Jun Lan, Zichang Tan, Ajian Liu, Chuanbiao Song, Senyuan Shi, Huijia Zhu, Weiqiang Wang, Jun Wan, Zhen Lei View a PDF of the paper titled Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning, by Hao Tan and 9 other authors View PDF HTML (experimental) Abstract:Deepfake detection remains a formidable challenge due to the complex and evolving nature of fake content in real-world scenarios. However, existing academic benchmarks suffer from severe discrepancies from industrial practice, typically featuring homogeneous training sources and low-quality testing images, which hinder the practical deployments of current detectors. To mitigate this gap, we introduce HydraFake, a dataset that simulates real-world challenges with hierarchical generalization testing. Specifically, HydraFake involves diversified deepfake techniques and in-the-wild forgeries, along with rigorous training and evaluation protocol, covering unseen model architectures, emerging forgery techniques and novel data domains. Building on this resource, we propose Veritas, a multi-modal large language model (MLLM) based deepfake detector. Different from vanilla chain-of-thought (CoT), we introduce pattern-aware reasoning that involves critical reasonin...