Solutions · Trust & safety

Deepfake & Synthetic Media Detection for Trust & Safety.

Fake profile photos and synthetic media scale faster than manual review. Detect AI-generated images, video and audio through one API before they reach your users.

  • Flag AI-generated profile photos and synthetic faces
  • Screen uploaded images, video and audio at scale
  • Integrate with your moderation pipeline via REST API
detection result
● LIKELY SYNTHETIC · 0.96
AI-generated profile photo blocked

Synthetic face detected at upload, before users saw it.

FaceGAN · 0.96
Asset reuseFLAGGED
ActionBLOCKED
7%
Of all global fraud is now deepfakes (Sumsub)
+180%
Rise in multi-technique attacks, 2025 (Sumsub)
1,151%
Rise in injection attacks, H2 2025 (iProov)
+244%
YoY growth in image/document forgery (Entrust)

Figures from Sumsub, Deloitte, FBI IC3, Pindrop, Entrust and iProov. Full data on our deepfake statistics page.

The problem

Synthetic accounts and media outscale manual review.

Dating apps, marketplaces and social platforms face AI-generated profile photos, synthetic media used for harassment and scams, and mass-produced fake content. Human review does not scale to the volume, and letting it through erodes user trust and invites abuse.

An automated deepfake and AI-image signal at the point of upload or profile creation lets moderation focus human attention where it matters most, and keeps synthetic media out before users ever see it.

How Deepfake Detector helps

Built for trust and safety teams.

Moderation queueimage · profiles
Synthetic face0.96 · FLAGGED
Reuse patternDETECTED
Batch anomalyHIGH

Synthetic profile photos, AI-generated images and cloned media scale past what manual content moderation can review. Deepfake Detector adds an automated deepfake signal to your platform integrity stack, so synthetic accounts and abusive media are flagged at scale.

Screen profile pictures, listings and user-generated content (UGC) for GAN-generated faces and diffusion-model artifacts, the kind used in romance scams, catfishing and marketplace fraud. Route high-confidence detections straight into your moderation queue through one REST API.

Profile & upload screening

Flag AI-generated faces and manipulated images at account creation or upload, across every major generator.

Built for scale

A REST API designed for programmatic, high-volume checks inside your existing moderation workflow.

Privacy-conscious

Files are purged after analysis, so screening media does not mean retaining it.

What it detects

One engine, every format.

The same detection engine covers video, image and audio.

Image

AI-generated faces and manipulated photos in profiles and uploads.

Video

Deepfake and synthetic video in user content.

Audio

Cloned voices and synthetic speech in voice content.

How it works

Three steps, seconds to a verdict.

1 · Submit

Send media to the API at upload, profile creation or report time.

2 · Analyze

Parallel forensic signals assess each item for AI generation and manipulation.

3 · Act

Use the verdict and confidence score to auto-flag, queue for review or block. Files are purged after the scan.

See the output

One request, one structured verdict.

Send media to the API and get a verdict, confidence score and the signals behind it.

~/curl · POST detect
$ curl https://app.deepfakedetector.ai/api/v1/detect/image \ -H "Authorization: Bearer sk_live_…" \ -F "file=@evidence.jpg" { "verdict": "likely_synthetic", "confidence": 0.96, "signals": ["gan_face", "reused_asset"] }
Why teams choose it

Accurate, fast, private.

High throughput

A REST API built for programmatic, high-volume moderation.

All formats

Image, video and audio across every major generator.

Consistent scoring

A verdict and confidence score your pipeline can route on.

No retention

Files are purged after analysis.

FAQ

Trust questions.

Everything teams ask before they roll it out. If yours isn't here, email hello@deepfakedetector.ai.

Can it handle high volumes?

The REST API is designed for programmatic, high-throughput use inside a moderation pipeline. Talk to our team about volume tiers.

What content types can it screen?

AI-generated and manipulated images, video and audio, covering profile photos, uploads and reported media.

Does screening mean you keep our users’ files?

No. Files are purged after analysis. See the Privacy Policy for the full data-handling detail.

Can it slot into our existing moderation stack?

Yes. It returns a verdict and confidence score via API that your pipeline can route on, alongside your current tooling.

How are new generators handled?

Coverage tracks the major generators as they evolve, so new models are added over time.

Related: Fake profile picture detection · Deepfake Detection API · AI Image Detector

Sources & methodology

Backed by current fraud research.

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.

REPORTSumsub, Identity Fraud Report 2025 to 2026
REPORTDeloitte Center for Financial Services, Generative AI and fraud (2024)
REPORTFBI Internet Crime Complaint Center (IC3), 2025 Annual Report
REPORTPindrop, 2025 Voice Intelligence and Security Report
REPORTEntrust, 2025 Identity Fraud Report; iProov, Threat Intelligence Report 2026
REVIEWEDWritten and fact-checked by the Deepfake Detector team, reviewed by Kevin, Lead Detection Engineer. Last reviewed July 2026. About us.
Related reading

Go deeper.

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at scale.

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