# Deepfake Detection > Deepfake Detection checks any image - a selfie, an ID photo, a damage photo, a screenshot of a document - for signs that it was generated by an AI model or that a face was swapped in. You get an authentic, AI-generated, face-swap or uncertain verdict, a risk score, and the specific findings behind it. **Canonical URL:** https://tampercheck.ai/deepfake-detection **Last updated:** 2026-11-11 --- ## Why a visual check no longer catches synthetic images Current image models produce photos with correct hands, plausible lighting and legible text. The tell-tale artefacts reviewers were trained to spot are mostly gone, and a face swap on a real photo leaves the rest of the scene genuine. **AI-generated images** were never captured by a camera. They lack the sensor noise, colour-filter pattern and lens behaviour every real photo carries, and many still carry provenance the generator wrote into the file. **Face swaps** start from a real photo, so whole-image checks can pass. The evidence sits at the blend boundary: skin texture, sharpness and lighting that change where the new face meets the original frame. Deepfake Detection is a separate strategy from Tamper Check. Tamper Check looks for edits to a genuine document; Deepfake Detection asks whether the image itself is synthetic. ## What TamperCheck detects ### Content credentials (C2PA) Reads signed C2PA manifests and validates the chain. A valid credential naming an AI tool is proof of generation, not a guess. ### Invisible AI watermarks Checks for the hidden pixel-level watermarks some generators embed, which survive screenshots and re-saves that strip metadata. ### Generator fingerprints Matches output dimensions, generation-parameter chunks, software tags and IPTC source types left by Midjourney, Stable Diffusion, DALL-E, Firefly and others. ### Camera-sensor forensics Measures sensor noise (PRNU), colour-filter-array patterns and chromatic aberration. Real cameras leave these; generators do not reproduce them faithfully. ### Face-swap blend analysis Inspects the face boundary for texture, sharpness and compression breaks, and compares face lighting with the shadows in the rest of the scene. ### Vision adjudication A vision model reviews the image alongside every forensic measurement - anatomy, text, reflections, physics - and explains its verdict in plain language. ## One upload, a clear verdict on whether the image is real Run Deepfake Detection from the dashboard for a single image, or set strategy to deepfake_detection on the API to check every selfie, ID photo or claim photo in your workflow. - **Four verdicts** - authentic, AI-generated, face swap or uncertain - **Images of any kind** - JPG, PNG, WebP, HEIC, screenshots and phone photos - **Provenance first** - signed credentials and watermarks settle the verdict when present - **Findings with regions** - see where the evidence is, not just a score - **Same API and dashboard** as Tamper Check - one parameter switches strategy - **Per-image pricing** - no subscription ## Frequently asked questions ### What does Deepfake Detection check? Whether an image was generated by an AI model or had a face swapped in. It reads content credentials and invisible watermarks, matches known generator fingerprints, measures camera-sensor noise and lens behaviour, analyses the face boundary for blending, and has a vision model weigh all of it. ### How is this different from Tamper Check? Tamper Check looks for edits to a genuine document - a changed balance, a re-typed date, a pasted signature. Deepfake Detection asks whether the image was ever captured by a camera at all, or whether the face in it is someone else's. Use Tamper Check for PDFs and documents, Deepfake Detection for photos and selfies. ### Does it work on PDFs? No. Deepfake Detection runs on images. For PDFs, Tamper Check already includes AI-generation signals for documents that were fabricated rather than edited. ### Can it tell which AI tool made the image? Often. When the image carries content credentials, a generator watermark, or generation metadata, the finding names the tool. When those have been stripped, the verdict rests on pixel-level evidence and the tool may not be identifiable. ### How do I use it through the API? Upload the image to the same documents endpoint you use for Tamper Check and set strategy to deepfake_detection. The response carries the verdict, risk score, confidence, findings and a plain-language summary. ## Related use cases - https://tampercheck.ai/use-cases/kyc-identity-verification - https://tampercheck.ai/use-cases/insurance-claims - https://tampercheck.ai/use-cases/hr-hiring ## Compare with alternatives - https://tampercheck.ai/compare/persona-vs-tampercheck-ai - https://tampercheck.ai/compare/veriff-vs-tampercheck-ai - https://tampercheck.ai/compare/sumsub-vs-tampercheck-ai --- Try TamperCheck now: https://tampercheck.ai/dashboard · Docs: https://tampercheck.ai/docs · Pricing: https://tampercheck.ai/pricing