One API for face swaps, fully synthetic imagery and cloned audio. Every decision returns a confidence score and a signed case file, with automatic escalation when the subject reads as a minor. EU-resident processing, zero raw-content retention.
No card required. Detection is probabilistic; every response carries a confidence score.
What Tuteliq detects
Synthetic media rarely arrives alone. Detections combine with the sextortion and grooming detectors so a manipulated image is scored against the conversation it appeared in.
Face swaps and identity manipulation
Forensic classifiers flag swapped, morphed and re-enacted faces in images and video frames, including outputs from consumer face-swap apps.
Fully synthetic and nudified imagery
Detects diffusion-model and GAN output, including images produced by nudify tools. Where the subject reads as a minor, the case escalates at maximum severity.
Voice clones and synthetic audio
Audio forensics for cloned and generated speech, used against voice-based grooming, impersonation and payment fraud in voice chat.
Video and livestream frames
Frame-sampled analysis for uploaded video and livestream segments, so manipulated media is caught before it fans out to other users.
The four questions buyers ask first
Latency, cost, data handling and time to ship. Answers up front, not after a sales call.
How fast is it?
Single image checks typically return inside a second; video is frame-sampled and scales with clip length. Detection runs inline on upload or asynchronously via webhook, whichever fits your write path.
What does it cost?
Usage is metered per request with modality weighting, so image checks cost less than video. Trials run 14 days with no card, and the calculator maps your monthly volume to a plan.
Where does my media go?
Processing is EU-resident and raw content is discarded after the decision. You keep a signed case record with model version, confidence and reviewer trail, not the file.
How do I ship it?
One REST endpoint, SDKs for the common runtimes, and an MCP server if your review workflow runs through an agent. Most teams have a first detection in production inside a day.
Generic classifiers vs provenance vs Tuteliq
Provenance metadata only works when the generator cooperates and the file survives intact. Generic classifiers score the pixels but ignore who the subject is.
Generic classifier
Watermark / C2PA
Tuteliq
Face-swapped images
Partial
Misses
Detects
Fully synthetic (diffusion) media
Only if watermarked
Nudified images of real people
Cloned voice audio
Minor-context escalation
No
Yes
Sextortion pattern correlation
Signed, auditable case file
Varies
Where teams deploy deepfake detection
Social and UGC platforms
Screen uploads and DMs before distribution, and tie a synthetic-media hit to the grooming or sextortion pattern around it.
Dating and companion apps
Catch synthetic profile photos and cloned voice notes used for romance fraud and coercion.
Gaming and voice chat
Detect voice cloning and manipulated clips shared in-session, where impersonation drives most harm.
Marketplaces and fintech
Flag manipulated identity documents and selfies during onboarding, alongside identity verification checks.
Deeper detail by segment: social platforms, dating apps, AI platforms.
Safeguards and compliance
Synthetic media decisions get challenged, so they need to be defensible. How Tuteliq handles that is documented in the Trust Center, compliance overview and AI transparency notes.
- Confidence score on every decision
- Model version recorded per case
- EU-resident processing
- Zero raw-content retention
- Human review and appeal workflows
- Signed, tamper-evident case files
Frequently asked questions
Ship deepfake detection this week
Start on the 14-day trial, point one endpoint at your upload path, and see scored detections against your own media before you commit.