Today marks the release of the second white paper in the Forensic Machine Learning Framework (FMLF) series:
Scientific Foundation Gap Analysis: Evaluating AI/ML‑Based Detection Methods for AI‑Generated Video in Forensic Science and Legal Contexts.

This paper examines a rapidly evolving challenge in digital and multimedia forensics: whether today’s AI/ML systems that claim to detect AI‑generated or manipulated video are scientifically reliable enough for use in investigative or legal settings. While research in this area is expanding quickly, most published work emphasizes benchmark performance or model‑specific improvements rather than the forensic expectations of transparency, reproducibility, documented error rates, and validated decision criteria.

This white paper applies methodological principles drawn from the NIST Scientific Foundation Review (SFR) framework to evaluate representative categories of video‑detection methods, including spatial‑artifact detectors, temporal‑consistency approaches, multimodal and hybrid pipelines, model‑specific fingerprinting techniques, and benchmark‑driven evaluations. The findings show that current approaches do not yet meet the standards required for forensic use, and the paper identifies the scientific gaps that must be addressed before these tools can be responsibly deployed.

The full paper is publicly available on OSF under a CC‑BY 4.0 license:

DOI: https://doi.org/10.17605/OSF.IO/R987T

This release continues the broader series of scientific‑foundation documents focused on forensic machine learning, following the earlier white paper on AI‑generated audio detection. Upcoming work in the series will address video ground‑truth methods, declared‑decoder workflows, and validation frameworks for multimedia evidence. More to come soon.