AI face-swaps and synthetic identities are built to pass liveness and document checks. Add a deepfake and manipulation check before an account is ever approved.
Figures from Sumsub, Deloitte, FBI IC3, Pindrop, Entrust and iProov. Full data on our deepfake statistics page.
Face-swap and fully synthetic faces are now good enough to clear many liveness and document checks, and injection attacks can feed pre-recorded or generated media straight into the verification flow. Traditional liveness confirms that a face is present and moving. It does not ask whether that face was generated by AI.
For regulated onboarding, one approved synthetic identity means fraud losses, remediation cost, and regulatory exposure. A deepfake-specific check adds a distinct signal layer that looks for the artifacts of AI generation and manipulation, alongside the identity stack you already run.
Face swaps and fully synthetic faces now clear many liveness and document checks. Deepfake Detector adds a deepfake-specific signal to your identity verification (IDV) and eKYC flow, so synthetic identity fraud is caught before an account is opened.
It runs alongside passive and active liveness detection and presentation attack detection (PAD), and flags injection attacks that feed pre-recorded or AI-generated media into the camera. Check a selfie, an ID document, or an onboarding video for the artifacts of face-swap and generative models, without adding friction for genuine users.
Analyze selfies and liveness frames for face-swap, morphing and GAN-generated faces across every major image and video generator.
Flag AI-manipulated or synthetically generated ID document images before they are accepted into your record of truth.
A single REST endpoint returns a verdict and confidence score you can gate approvals on, with no rip-and-replace of your IDV vendor.
The same detection engine covers video, image and audio.
Deepfake and face-swap manipulation in liveness clips and video KYC sessions.
AI-generated faces and edited or composited ID documents.
Synthetic and cloned speech in voice-based verification steps.
POST the selfie, liveness frame or document image to the API from inside your onboarding step.
Multiple forensic signals run in parallel, tuned to the artifacts of AI generation and editing.
Get a clear verdict and confidence score back in seconds to allow, review or block. Files are purged after the scan.
Send media to the API and get a verdict, confidence score and the signals behind it.
Consensus across signal layers, not a single coin-flip model.
Image, video and audio, across every major generator.
Files are purged after analysis. Screening media does not mean retaining it.
One REST API that slots beside your existing IDV and liveness vendors.
Everything teams ask before they roll it out. If yours isn't here, email hello@deepfakedetector.ai.
Yes. The detector analyzes image and video frames for the signals left by face-swap and AI face generation, which is a different question than whether a live person is present. It runs alongside your existing liveness vendor as an added signal layer.
No. It complements your IDV and liveness stack. You keep your provider and add a deepfake-specific check via API at the point where you already have the media.
Images, video and audio, so it covers selfies, document photos, liveness clips and voice-based verification.
Most files return a verdict with a confidence score in seconds, so a check fits inside a live onboarding step.
Files are purged after analysis. See our Privacy Policy for the full data-handling detail before you integrate.
Related: KYC deepfake detection guide · Deepfake Detection API · AI Image Detector
The figures on this page are drawn from primary industry reports, not our own marketing. Full data, definitions and links are on our deepfake statistics page.
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