AI Deepfake Forensics Analysis

Two rules govern. Federal Rule of Evidence 901 requires the proponent to produce evidence sufficient to support a finding that the recording is what it is claimed to be, and its own examples — distinctive characteristics taken with all the circumstances, and evidence that a process or system produces an accurate result — map directly onto provenance work and validated content analysis; Lorraine v. Markel remains the roadmap for how electronically stored information clears that hurdle and the others that follow it. Federal Rule of Evidence 702 then requires the expert's opinion to rest on sufficient facts, reliable methods and a reliable application of them, demonstrated by a preponderance since the December 2023 amendment; Daubert v. Merrell Dow makes the court the gatekeeper and Kumho Tire v. Carmichael applies that gate to technical work of exactly this kind.

The Daubert factors are harder to satisfy in this field than in most, because detection moves faster than the case schedule and a detector trained on one generation of models degrades against the next. NIST AI 100-4, Reducing Risks Posed by Synthetic Content (November 2024), which surveys provenance tracking, watermarking and detection techniques, states that "the efficacy of many of these technical approaches are not fully examined yet" and that none of them "offer comprehensive solutions on their own" (NIST AI 100-4). An expert whose entire opinion is a black-box classifier score has staked the opinion on a tool whose error rate against the specific model used is unknown, and should expect exclusion. The defensible opinion is narrower and more useful, and it is the one we write: multiple independent indicators, each with its basis stated, and the limits stated just as plainly. Where the other side's expert has offered the score as the opinion, our Daubert challenge defense work documents the gap.

The record shows the method at scale: in a deepfake content-ring investigation for a global streaming platform, the examination identified more than 1,400 fraudulent works, supported asset freezes against the shell companies behind them and criminal referrals in three countries, and the detection methodology was subsequently licensed by the platform for its own content-authentication operations. The same provenance-first approach governs how a screenshot is authenticated and how social media evidence is collected.

Law & Forensics is a bench of seven named experts, and media-authentication matters draw on the examiners and the platform operators among them. J-Michael Roberts, a Senior Director and Certified Computer Examiner, has testified in federal court to device analysis that proved the manipulation and forgery of evidence, and has interrogated the tools themselves — identifying a flaw in a commercial forensic tool that cleared an accused individual. Daniel B. Garrie, the firm's founder, holds computer-science degrees alongside his law degree, is a co-inventor on forensic patents, and testifies as a digital forensics expert in state and federal court. Roland Cloutier, formerly Global Chief Security Officer of ByteDance and TikTok, has overseen the protection of platforms carrying more than a billion users and the investigative operations behind them, and speaks to how synthetic content moves through and is moderated on a platform. David Cass has published on AI and machine learning alongside forensics and teaches computer forensics at Harvard. See the full expert panel.

Where a recording's authenticity is likely to be contested but no motion has been filed, the Evidence Readiness Assessment is the fixed-fee, fixed-scope engagement that identifies which copies and devices exist, which originals can still be obtained, what account and platform records corroborate the file, and what is at risk of loss — so the discovery demands for originals, devices and account records are made while the schedule still allows them. It is prepared by the same examiners who testify, and it shapes the analysis rather than following it.