Updated: September 3, 2026 · Docs · Clothoff AI Editorial Team
How does deepfake detection work?
A glossary entry on the methods used to identify manipulated or generated media, their known error rates, and the practical steps for a person depicted in a fake.
Educational glossary entry by a review publisher; not legal advice.
Which detection methods exist?
Deepfake detection is the set of methods used to decide whether an image, video or audio clip was generated or manipulated by AI. NIST groups them into 3 families: provenance data such as watermarks and Content Credentials, automated content analysis that looks for artifacts, and human-assisted review. No method is reliable on its own, so results are probabilities, not proof.
Each family answers a different question, and each has a characteristic failure mode that matters when an image of a real person is at stake.
| Method | What it checks | Main weakness |
|---|---|---|
| Provenance (watermark, credential) | Whether a known mark or signed record is present | Absence proves nothing; marks can be stripped |
| Artifact classifier | Statistical traces left by generators | Degrades on new models and compressed files |
| Hash / fingerprint matching | Whether a file matches a known image | Needs a prior report of the exact image |
| Human-assisted review | Context, lighting, anatomy, source checks | Slow and subjective; realistic fakes pass |
What can provenance data tell you?
If an image carries a SynthID signal or a C2PA credential naming a generative model, the question is settled. The problem is coverage: most generators mark nothing and social uploads strip metadata, so a real-world image usually arrives with no provenance.
Hash matching works the other way around. Services such as StopNCII create a fingerprint of an image on the victim’s device and share only that hash with participating platforms, which then remove matching copies. It cannot find edited variants that no one has reported.
How accurate are detector tools?
Artifact classifiers learn the statistical fingerprints of known generators. They perform well on the models they were trained on and worse on newer ones, and compression, resizing and screenshots erase many traces. Vendors rarely publish error rates on independent test sets.
For a single image the practical implication is simple: a “likely AI” score supports suspicion but is not evidence in itself, and a “likely real” score can be wrong. Platforms therefore pair classifiers with reports, context and human review before acting.
What should a person shown in a fake do?
Under the TAKE IT DOWN Act, covered platforms must remove a reported nonconsensual intimate image, including a computer-generated one, within 48 hours of a valid request; the duty has been enforceable since May 19, 2026. Our guide what to do about deepfake nudes walks through the steps.
Keep the URL and a screenshot, file the platform report, and add a StopNCII hash so copies are caught elsewhere. If the image appears in connection with a tool we review, the NCII report form lets us flag it to the provider and document the response.
“Synthetic content detection: classifying whether content is synthetic using provenance data, automated content-based methods, or human-assisted detection.”
— NIST AI 100-4, Reducing Risks Posed by Synthetic Content (November 2024), accessed September 3, 2026
Consent Note
This site is a publisher: it reviews third-party tools and does not create, edit, accept or store images. Docs entries are general information, not legal advice.
Sources
All sources accessed on September 3, 2026; educational entry, not legal advice.
FAQ: Deepfake detection
Can software tell for certain whether an image is AI-generated?
No. A detected watermark or credential is strong evidence of generation, but their absence proves nothing, and artifact classifiers output probabilities that fall on new models and compressed files. Treat any single score as one signal to be combined with source checks and context.
Are there free deepfake detectors I can use?
Several browser tools and research demos exist, and Gemini can check for SynthID in Google-generated images. Results vary widely between tools and on the same image after compression. None publishes audited accuracy on current generators, so use them as a first pass rather than a verdict.
What signs suggest an undress-style fake?
Look for inconsistent lighting between face and body, mismatched skin tone at the neckline, blurred or repeated texture where clothing was, and warped jewelry or hands. These cues are unreliable on high-quality output; a reverse image search for the original photo is often more decisive.
How fast must a platform remove a reported fake?
Under the TAKE IT DOWN Act, a covered platform must remove a reported nonconsensual intimate image, including a digital forgery, as soon as possible and no later than 48 hours after a valid request, and make reasonable efforts to remove identical copies. The FTC enforces the requirement.
Does StopNCII work for AI-generated images?
Yes, if you have the image file. The hash is generated on your device, only the hash leaves it, and participating platforms remove matches. It catches identical or near-identical copies, not new variants, so combine it with platform reports for each distinct image you find.
Do you run a detector on samples in reviews?
We check whether downloaded output carries a watermark or credential and record the result in the Editorial Test Notes. We do not publish classifier scores, because vendors and tools change monthly and a number without an audited method would be misleading.