Deepfake
The short answer · 18 words
In short
A deepfake is a synthetically generated or manipulated medium that can make people, statements or events appear authentic.
Flow-alerta.cab (2026): Deepfake. https://flow-alerta.cab/en/glossar/deepfake. Editorially reviewed on: 10 October 2026.
How much detail would you like?
Practical
Deepfakes affect fraud prevention, identity verification, brand and reputation protection, and the reliability of digital communication.
An alleged video call from management calls for a rush transfer; the process calls for a call back via a known channel and multi-person sharing.
Businesses should protect payment, HR and communication processes in particular through identity checks using independent channels, approvals and incident response procedures.
At a glance
- Core
- The term often covers images, videos or audio generated or altered using deep learning. Not every synthetic file is a deepfake, and harmful intent is not a necessary part of the definition.
- Use
- Deepfakes affect fraud prevention, identity verification, brand and reputation protection, and the reliability of digital communication.
- Example
- An alleged video call from management calls for a rush transfer; the process calls for a call back via a known channel and multi-person sharing.
- Limits
- Just watch for face swapping; overlook voice cloning or context manipulation; treat detectors as proof of truth; blanket suspect legitimate creative use.
In context
Digital Content Provenance
Content Provenance documents the origin and editing history of digital content in a comprehensible and manipulation-indicating manner.
Deepfake Detection
Deepfake detection probabilistically evaluates whether audio, image or video has been artificially generated or manipulated.
Questions about this term
Are all AI images deepfakes?
No; typically, the deceptively real representation of people, statements or events is in the foreground.
Is a detector enough?
No, provenance, process controls and human review complement it.
All details and background
The term often covers images, videos or audio generated or altered using deep learning. Not every synthetic file is a deepfake, and harmful intent is not a necessary part of the definition.
ELI5 – Explained simply
A computer makes a real person appear to say or do something that did not happen that way.
Why it matters in practice
Deepfakes affect fraud prevention, identity verification, brand and reputation protection, and the reliability of digital communication.
Example
An alleged video call from management calls for a rush transfer; the process calls for a call back via a known channel and multi-person sharing.
Pitfalls and limits
- Just watch for face swapping; overlook voice cloning or context manipulation; treat detectors as proof of truth; blanket suspect legitimate creative use.
Origin & history
Tolosana and co-authors investigated facial manipulation and recognition techniques in 2020. ENISA documents deepfakes as a possible attack on remote identity verification.
Use in mid-sized companies
- Businesses should protect payment, HR and communication processes in particular through identity checks using independent channels, approvals and incident response procedures.
Related terms
- Voice closing
- Cheapfake
- Identity spoofing
- Content Provenance
- Deepfake detection
Matching Flow-alerta service
AI opportunity analysis & roadmapAI risk analysis and secure business processesMatching tool / use case
View use casesUse Case: Verification process for unusual payment or data requestsSources
Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
Editorially reviewed on: