Deepfake
A deepfake is image, audio, or video content generated or manipulated with AI so that a person, object, place, entity, or event appears authentic even though the depicted action, statement, or occurrence did not happen that way. Deepfakes are part of the wider field of synthetic and manipulated media. Not every synthetic image or ordinary edit is a deepfake; deceptive resemblance to an authentic subject or event is central.
Origin and context
In December 2017, Motherboard reported on machine-learning face swaps posted by a Reddit user using the handle deepfakes. The label spread from that specific non-consensual use into a broader technical and policy category covering visual and audio impersonation. A 2020 survey systematized creation and detection research. The EU AI Act now gives deep fake a legal definition, while NIST treats deepfakes within the broader synthetic-content transparency problem.
Why it matters
Deepfakes can support fraud, impersonation, harassment, non-consensual intimate imagery, and political deception, while similar techniques also have consensual creative and accessibility uses. The same output may engage privacy, publicity, consumer-protection, election, platform, or AI-specific rules depending on context. Reliable response therefore needs provenance, detection, disclosure, consent, and incident processes rather than an assumption that one classifier can decide authenticity.
Example
A video makes a public official appear to announce a policy they never discussed. Reviewers compare the media with authoritative footage, inspect provenance credentials and editing history, use detection tools as supporting evidence, and assess distribution context. If the content is AI-generated or manipulated and falsely appears authentic, it fits the deepfake category. A clearly labeled fictional avatar that does not impersonate an authentic event may instead be ordinary synthetic media.
How it differs
Real-time deepfakes (Live deepfakes)
A real-time or live deepfake is a delivery subtype produced or applied during an interaction, such as a video call. Latency changes detection and response needs, but not the core concept. It belongs under the deepfake entry as a reference-only companion, not as a full synonym.
Content provenance and C2PA
Content credentials, provenance metadata, and watermarking are transparency or authenticity mechanisms. They can help establish origin and editing history, but absence of a credential does not prove a deepfake and presence of a marker does not resolve every question about consent, context, or truth.
Maturity and evidence
Maturity is rated 5. The term has a documented 2017 origin, extensive technical literature, broad public use, and an explicit definition in the EU AI Act. Technical and legal boundaries still vary: some regimes focus on persons, others include entities or events, and disclosure duties depend on use. The canonical definition therefore states the shared core without claiming one global rule.
Limits and open questions
Detection performance changes as generation and compression methods evolve, and false positives can harm authentic speakers. Provenance can be removed or unavailable for legacy content. The term is also used loosely for satire, cheap edits, and any synthetic media, which can obscure the actual technique and harm. Assessments should identify what was generated or manipulated, whether authenticity is implied, who is depicted, how the content was distributed, and which jurisdiction and disclosure rule applies.
Related terms
References
- AI-Assisted Fake Porn Is Here and We're All FuckedMotherboard / VICE · 2017-12-11 · class B
- Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligenceOfficial Journal of the European Union · 2024-07-12 · class A
- Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content TransparencyNational Institute of Standards and Technology · 2024-11-20 · class A
- The Creation and Detection of Deepfakes: A SurveyACM Computing Surveys / arXiv · 2020-04-23 · class A
Last updated: 2026-09-04
This term is also covered in the Skills Atlas as computer vision skill.
This term is also covered in the Skills Atlas as ai watermarking skill.
This term is also covered in the Skills Atlas as ai ethics skill.