Deepfake Detection

Deepfake detection tools analyze media for signs that a face, image, video, voice or document was generated or manipulated with AI. They can support identity verification, fraud prevention, content moderation and forensic review, but their output is normally a risk signal rather than proof on its own.

Match the detector to the media and threat

Check whether a product accepts still images, recorded video, live streams, audio or document scans. A system built for face swaps may not detect voice cloning or synthetic documents. For identity workflows, examine presentation attacks, replayed screens, virtual cameras and injection attacks as well as generated faces.

Compare decisions and integration

Useful products explain the verdict with confidence, reason codes or highlighted evidence. Compare browser tools, cloud APIs, mobile and web SDKs, batch processing, latency, webhooks and review queues. Test both clear fakes and difficult genuine samples from your own capture conditions.

Understand limits and data handling

Detection quality changes as generators evolve, compression removes artifacts and lighting or camera quality varies. Look for current evaluations, false-positive information and escalation options. Before uploading sensitive media, review retention, training use, encryption, deletion and regional processing. Do not treat one score as legal proof or automatic grounds for a high-impact decision.