Deepfakes in Conflict Zones: How AI-Generated Disinformation Affects Refugee Crises

The threat is now operational, not theoretical

Through 2022, deepfakes in conflict information environments were mostly low-effort, easily debunked, and short-lived. The Zelensky 'surrender' video of March 2022 was the canonical early example: crude, removed within hours, but viewed millions of times before takedown. By 2026, generative video and audio quality have improved enough that visual debunking is no longer reliable; provenance and metadata analysis are doing the work. The Reuters Institute and the Atlantic Council DFRLab document a steady increase in synthetic-media incidents in conflict contexts.

How deepfakes affect refugee crises specifically

Three impact pathways recur. Atrocity denial: synthetic 'evidence' that an attack did not occur, or that footage of one is staged, is now a standard information-operations playbook. Manufactured atrocity: fabricated content designed to escalate conflict, mobilise mob violence, or justify state action against a refugee population. Targeted impersonation: synthetic videos of named refugee leaders, journalists, or humanitarian workers used to discredit them. All three increase the verification burden on serious reporting and decrease the half-life of credible information.

The 2026 verification toolkit

Newsroom verification has converged on a stack. Provenance: C2PA Content Credentials where present, SynthID watermark detection where applicable. Reverse image search: Google, Bing, Yandex, TinEye. Geolocation: cross-referencing against satellite imagery and street-view archives. Chronolocation: shadow analysis, weather records via Wolfram Alpha and Visual Crossing. Audio forensics: spectral analysis for splices and synthesis artefacts. Source vetting: provenance of the original uploader, account history, network of amplification. The Bellingcat online investigation toolkit catalogues the operational tools.

AI detectors are not the answer they were sold as

Commercial deepfake detectors have a consistent track record of false confidence in both directions. They fail open on novel generators and fail closed on heavily compressed legitimate footage. The GenAI Detect Challenge results at NIST show meaningful but limited progress. The defensible newsroom workflow in 2026 treats detector output as one signal among many, never as a verdict.

What humanitarian organisations should do

Three operational responses are emerging as good practice. Provenance-first communications: signed and credentialed video of organisational statements and field testimony reduces the impersonation surface. Rapid response protocols with social platforms for takedown of synthetic content depicting staff or beneficiaries. Media literacy programmes in refugee communities themselves, where targeted disinformation can directly affect safety. Internews and Tactical Tech lead public work in this area.

What reporters should ask before publishing conflict imagery

- Who uploaded the original, and when? - Does it carry content credentials? - Can it be geolocated and chronolocated? - Does any element (shadows, weather, foliage, signage) contradict the claimed time or place? - Are there independent sources of the same event from a different vantage? A 'no' on the last point is not disqualifying but should be disclosed.

Further reading and primary sources

• C2PA: https://c2pa.org/
• DFRLab: https://www.atlanticcouncil.org/programs/digital-forensic-research-lab/
• Bellingcat toolkit: https://bellingcat.gitbook.io/toolkit/
• Reuters Institute: https://reutersinstitute.politics.ox.ac.uk/
• NIST media forensics: https://www.nist.gov/itl/iad/mig/media-forensics-challenge
• Internews: https://internews.org/