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Advancing reliable synthetic video detection: Insights from the SAFE challenge
Journal article   Open access   Peer reviewed

Advancing reliable synthetic video detection: Insights from the SAFE challenge

Kirill Trapeznikov, Gabriel Mancino-Ball, Jonathan Li, Paul Cummer, Jai Aslam, Danial Samadi Vahdati, Tai Nguyen, Matthew C. Stamm, Peter Bautista, Michael Davinroy, …
Forensic science international. Digital investigation (Online), v 57, 302120
01 Jun 2026
DOI: https://doi.org/10.1016/j.fsidi.2026.302120
url
https://doi.org/10.1016/j.fsidi.2026.302120View
Published, Version of Record (VoR) Open

Abstract

The proliferation of generative video technologies has intensified the need for reliable methods to detect and characterize synthetic media. To address this challenge, we organized the SAFE: Synthetic Video Detection Challenge, co-located with the Authenticity and Provenance in the Age of Generative AI (APAI) Workshop at ICCV 2025. The competition invited participants to develop and evaluate algorithms capable of distinguishing real from synthetic videos under fully blind evaluation conditions with over 600 submissions from 12 teams over a 90 day span. Hosted on the Hugging Face platform, the challenge comprised two primary tasks: (1) detection of synthetic video content generated by diverse state-of-the-art models, and (2) detection of synthetic content following common post-processing operations such as resizing, re-compression, motion blur and others. The challenge data consisted of 13 modern high quality synthetic video models with generated content matched to real videos from 21 diverse and challenge sources, all adding up to 20 h of 6000 video samples. This paper describes the challenge design, dataset construction, evaluation methodology, and outcomes, offering insights into the generalization and robustness of contemporary synthetic video detection methods. Our findings highlight measurable progress in cross-generator generalization but also persistent vulnerabilities to post-processing artifacts. https://safe-video-2025.dsri.org.

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