How to detect deepfake video
This guide explains how to evaluate deepfake video without relying on a single visual clue or detector score. The strongest workflow combines provenance, forensic signals, context, and a documented review decision.
Report example
deepfake video review example
- Check lip-sync and facial boundaries
- Review frame-to-frame consistency
- Compare audio and video alignment
- Request original source material
Search intent
Informational search intent for deepfake video
Primary evidence
Check lip-sync and facial boundaries, Review frame-to-frame consistency, Compare audio and video alignment
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
First-pass checks
Start with indicators that are fast to inspect and low-risk to document.
- Check lip-sync and facial boundaries
- Review frame-to-frame consistency
- Compare audio and video alignment
- Request original source material
Detector-assisted review
Use a detector to identify patterns that are hard to inspect manually, then validate the output with source context.
- Check whether evidence is localized or global.
- Compare detector confidence with metadata and provenance.
- Keep borderline cases in a manual review queue.
When not to overclaim
Compression, editing, templates, translation, and human post-production can create signals that resemble synthetic artifacts.
- Avoid public accusations from a single automated result.
- Use confidence bands, not absolute language.
- Document what evidence was present and what was missing.
Use cases
Newsroom verification before publication.
Marketplace or platform review of suspicious media.
Enterprise fraud and impersonation triage.
Sample report preview
Media preview
Safe sample, redacted upload, or generated demonstration asset.
Public reports should only expose media that is lawful, consented, and safe to publish.
Confidence
Manual review recommended
Check lip-sync and facial boundaries
Evidence item linked to score calibration, source context, and known uncertainty.
Review frame-to-frame consistency
Evidence item linked to score calibration, source context, and known uncertainty.
Compare audio and video alignment
Evidence item linked to score calibration, source context, and known uncertainty.
Request original source material
Evidence item linked to score calibration, source context, and known uncertainty.
Evaluation table
| Criterion | What to check | Why it matters |
|---|---|---|
| Coverage | Text, image, audio, video, code. | Synthetic media risk rarely stays in one format. |
| Explainability | Score, indicators, timestamps, metadata, limitations. | Reviewers need evidence, not a black-box verdict. |
| Accuracy risk | False positives, false negatives, calibration. | High-impact workflows require documented uncertainty. |
| Workflow fit | API, batch, reports, retention, reviewer queues. | Search traffic must convert into a usable product path. |
Methodology and limitations
How to read the score
Detection output should be read as calibrated evidence. A high score means the observed signals are consistent with synthetic or manipulated media under the current model and sample conditions. It does not prove authorship, intent, or model attribution by itself.
Where review is required
Short samples, heavy editing, compression, translation, re-recording, mixed human-AI content, and unseen generators can reduce confidence. Use human review, source context, and policy thresholds before high-impact enforcement.
Next step
Match the action to the visitor intent: detector pages should lead to a scan, research pages to a downloadable report, enterprise pages to a demo, and developer pages to API keys or playground examples.
FAQ
Can manual inspection replace detection tools?
Manual inspection is useful, but many synthetic signals are subtle or hidden in metadata, frequency patterns, or frame-level inconsistencies.
What should I do with an uncertain result?
Preserve the evidence, request source material when possible, and route the case to human review instead of making a final claim.
Why do detectors disagree?
Detectors use different training data, features, thresholds, and modality coverage, so disagreement is expected on ambiguous samples.