03 · Verification & Forensics |
Deepfake Detection | |||||||
Image Witness · Hive Moderation · Sensity · Adobe Content Authenticity · FFmpeg | ||||||||
Intermediate | ||||||||
Audio intelligence: location and context from sound. See also: reverse image search and facial comparison and using AI tools in the verification workflow for the reference-card versions of the provenance-tracing and AI-triage steps covered here. |
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AI-generated video and audio now circulate routinely in conflict zones, election campaigns and financial fraud. The production barrier has collapsed: a convincing voice clone costs nothing and takes minutes, and a face-swap that would have required a studio in 2019 now runs on a consumer laptop. The investigative consequence is that any unverified media must be treated as potentially synthetic until the evidence says otherwise.
Deepfake detection is not a single test. It is a layered workflow combining tool-based scoring, human perceptual analysis and provenance interrogation; no single detector is definitive, and each can be defeated by a sufficiently motivated adversary. The value is in corroboration across layers: a clip that passes one test but fails three others warrants a different editorial judgement than one that fails all four.
In the field Two days before Slovakia's September 2023 parliamentary election, an audio clip circulated on Facebook, Instagram and Telegram purporting to record a phone call between opposition leader Michal Šimečka and journalist Monika Tódová from Denník N, discussing ballot manipulation. Both subjects immediately denied it. AFP fact-checker Robert Barca, who monitors Slovak Facebook content, identified manipulation artefacts in the recording, including unnatural diction, pauses and tone of voice, and traced its spread to an anonymous Telegram account. AFP flagged the posts to Meta before the election.
AFP Fact Check · Slovakia parliamentary election · September 2023 Note: a separate Slovak deepfake clip that same week (Progressive Slovakia leader Šimečka discussing beer prices) was verified by fact-checking organisation Demagog, whose team contacted ElevenLabs directly for expert comment on the synthesis fingerprint. That consultation applied to the beer-prices clip, not the ballot-manipulation clip above; the two are documented separately and should not be conflated. |
Learning outcomes
By the end of this tutorial you will be able to:
Identify the visual, acoustic and statistical artefact classes that distinguish synthetic media from authentic recordings
Run a structured deepfake detection workflow across video and audio using four free or low-cost tools
Interpret detection scores correctly, including the limitations and failure modes of each tool
Conduct a provenance interrogation to establish whether a clip has a traceable origin
Document detection findings to an evidential standard that supports editorial or legal review
The rest of this tutorial is for Signal subscribers.
What remains: the decision framework, the tool configuration, the failure modes, and the evidentiary standard required to use the finding defensibly. Signal is €90 a year, or €9 a month. Students, €49 a year.
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