SynthForensics: A Multi-Generator Benchmark for Detecting Synthetic Video Deepfakes

• URL: http://arxiv.org/abs/2602.04939v1
• Date: Wed, 04 Feb 2026 16:47:37 GMT
• Title: SynthForensics: A Multi-Generator Benchmark for Detecting Synthetic Video Deepfakes
• Abstract summary: We introduce SynthForensics, to the best of our knowledge the first human-centric benchmark for detecting purely synthetic video deepfakes.<n>The benchmark comprises 6,815 unique videos from five architecturally distinct, state-of-the-art open-source T2V models.<n>We observe a mean performance drop of $29.19%$ AUC, with some methods performing worse than random chance, and top models losing over 30 points under heavy compression.
• Score: 5.341795386333291
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The landscape of synthetic media has been irrevocably altered by text-to-video (T2V) models, whose outputs are rapidly approaching indistinguishability from reality. Critically, this technology is no longer confined to large-scale labs; the proliferation of efficient, open-source generators is democratizing the ability to create high-fidelity synthetic content on consumer-grade hardware. This makes existing face-centric and manipulation-based benchmarks obsolete. To address this urgent threat, we introduce SynthForensics, to the best of our knowledge the first human-centric benchmark for detecting purely synthetic video deepfakes. The benchmark comprises 6,815 unique videos from five architecturally distinct, state-of-the-art open-source T2V models. Its construction was underpinned by a meticulous two-stage, human-in-the-loop validation to ensure high semantic and visual quality. Each video is provided in four versions (raw, lossless, light, and heavy compression) to enable real-world robustness testing. Experiments demonstrate that state-of-the-art detectors are both fragile and exhibit limited generalization when evaluated on this new domain: we observe a mean performance drop of $29.19\%$ AUC, with some methods performing worse than random chance, and top models losing over 30 points under heavy compression. The paper further investigates the efficacy of training on SynthForensics as a means to mitigate these observed performance gaps, achieving robust generalization to unseen generators ($93.81\%$ AUC), though at the cost of reduced backward compatibility with traditional manipulation-based deepfakes. The complete dataset and all generation metadata, including the specific prompts and inference parameters for every video, will be made publicly available at [link anonymized for review].

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