Deepfake
A deepfake is synthetic or manipulated media that makes a person appear to say or do something they did not say or do.
Report example
Deepfake knowledge note
- Face swaps
- Lip-sync manipulation
- Voice and video impersonation
- Generated public figure clips
Search intent
Glossary definition for Deepfake
Primary evidence
Face swaps, Lip-sync manipulation, Voice and video impersonation
Recommended action
Use confidence scores with source context, policy thresholds, and human review.
Definition
A deepfake is synthetic or manipulated media that makes a person appear to say or do something they did not say or do.
- Face swaps
- Lip-sync manipulation
- Voice and video impersonation
- Generated public figure clips
Why it matters for detection
Deepfake affects how teams interpret evidence, route review decisions, and explain authenticity findings to users or stakeholders.
- It can change which signals are reliable.
- It often requires context outside the media file.
- It should be represented clearly in review reports.
Related concepts
Synthetic media detection is strongest when glossary concepts are connected to concrete review workflows.
- Confidence scoring
- Provenance and metadata
- Human review and appeals
Use cases
Reviewer training
Policy documentation
Technical onboarding for authenticity workflows
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
Concept coverage: defined
Face swaps
Evidence item linked to score calibration, source context, and known uncertainty.
Lip-sync manipulation
Evidence item linked to score calibration, source context, and known uncertainty.
Voice and video impersonation
Evidence item linked to score calibration, source context, and known uncertainty.
Generated public figure clips
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
Is deepfake always malicious?
No. Many synthetic media techniques have legitimate uses. Risk depends on disclosure, consent, context, and downstream harm.
How does ZeroTrue use this concept?
ZeroTrue connects glossary concepts to evidence in detection reports so reviewers can understand why a signal matters.
Why include glossary pages?
They help users, search engines, and AI answer systems understand the domain vocabulary around authenticity and synthetic media.