How did fact‑checkers determine the protester video was AI‑generated, and what indicators distinguish deepfakes from real footage?

1. How fact‑checkers reached the verdict: tools plus human judgment

Teams first ran the clip through synthetic‑media detectors such as Hive AI and Wasitai, which flagged the asset as fully AI‑generated, and then paired those tool results with reverse image/video searches and contextual checks to look for a traceable source — a workflow mirrored by CyberPeace and others in the Iran case ^[1]. Fact‑checkers emphasise that automated detectors give probabilistic scores rather than absolute proof, so reporters corroborated the tool outputs with visual analysis and sourcing because modern detection is about likelihood and editorial judgment, not a binary test ^{[3] [4]}.

2. Visual indicators that raised red flags

Observers highlighted several visual oddities consistent with synthetic generation: unnatural bird’s‑eye or wide panoramic angles that exaggerate crowd scale, indistinct or malformed faces, patterned or repetitive flashlight behavior, and moments where people appear to merge with flags or background elements — all described by DW’s verification team as characteristic of AI manufacture ^[2]. Other outlets noted that AI fakes can look “suspiciously clean” or glossy compared with real handheld protest footage, a stylistic giveaway that something was produced in the same synthetic aesthetic ^[5].

3. Temporal and contextual inconsistencies

Fact‑checkers also test whether a clip’s situational logic holds: does lighting, movement, or environmental continuity make sense across frames? The GIJN guide warns generated content “looks convincing in isolation but falls apart under sensible scrutiny,” and MIT’s DetectFakes project stresses that temporal and contextual mismatches are recurring telltales because models lack real‑world understanding ^{[3] [4]}. Where a clip can’t be geolocated, dated, or linked to independent eyewitness media, that absence compounds suspicion ^[2].

4. Metadata, provenance and reverse searches

Practical verification hinges on provenance: reverse image and video searches, checking watermarks or account history, and looking for earlier uploads or the original creator ^{[6] [7]}. When those traces are missing and AI‑detectors independently flag the asset, fact‑checkers treat the claim as likely fabricated; BBC Verify and others explicitly combine open‑source intelligence and satellite or field reporting where possible to ground or debunk viral video claims ^{[8] [7]}.

5. Why no single indicator is decisive — and how fact‑checking adapts

Research projects and verification platforms insist there is no single tell‑tale sign of fakery; instead, the field uses ensembles of indicators and evolving tools, acknowledging false negatives and positives are possible ^{[4] [3]}. Newsrooms are experimenting with workflows and AI assistance — from CheckMate’s claim‑extraction tools to Reuters Institute‑backed classifiers — to speed up multi‑signal checks because traditional manual verification can’t keep pace with minute‑scale synthetic production ^{[9] [10]}.

6. The limits and competing narratives

Some social platforms and users employ generative AI to debunk as well as to deceive, and reliance on unsupervised AI for fact‑checking can itself be risky, per TIME and verification practitioners who warn that models are not replacements for field reporting ^[11]. In short, the verdict that a protest clip was AI‑generated rested on converging evidence — detection tool scores, visual artifacts, provenance gaps and contextual implausibilities — and not on any one technique alone ^{[1] [2]}.