Blockiere KI-Agenten, generierte Gesichter, Deepfakes und doppelte Konten von menschlichen Oberflächen. iBeta Level 1 PAD-zertifizierte passive Liveness plus kostenlose 1:N Deduplizierung. 0,10 $ pro Prüfung, 500 jeden Monat kostenlos.
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Bots blockieren · Deepfakes stoppen · Sybil-Angriffe deduplizieren
Passive Liveness lehnt jede Kategorie von Presentation Attack Detection (PAD) ab – Druck, Bildschirm, Maske, KI-generiertes Gesicht. Face Search 1:N erkennt dann dieselbe Person, die versucht, sich zweimal zu registrieren. Ergebnis in unter 2 Sekunden. $0.10 pro Prüfung. 500 Verifizierungen jeden Monat kostenlos, für immer.
Wähle die gewünschten Prüfungen aus, ID, Liveness, Gesichtsabgleich, Sanktionen, Adresse, Alter, Telefon, E-Mail, benutzerdefinierte Fragen. Ziehe sie im Dashboard in einen Flow oder poste denselben Flow an unsere API. Verzweige nach Bedingungen, führe A/B-Tests durch, kein Code erforderlich.
Bette nativ mit unseren Web-, iOS-, Android-, React Native- oder Flutter-SDKs ein. Leite auf eine gehostete Seite um. Oder sende deinem Nutzer einfach einen Link, per E-Mail, SMS, WhatsApp, überall. Wähle, was zu deinem Stack passt.
Didit hostet die Kamera, die Beleuchtungshinweise, die mobile Übergabe und die Barrierefreiheit. Während der Nutzer den Flow durchläuft, bewerten wir über 200 Betrugssignale in Echtzeit und verifizieren jedes Feld anhand autoritativer Datenquellen. Ergebnis in unter zwei Sekunden.
Echtzeit-signierte Webhooks halten deine Datenbank synchron, sobald ein Nutzer genehmigt, abgelehnt oder zur Überprüfung gesendet wird. Frage die API bei Bedarf ab. Oder öffne die Konsole, um jede Session, jedes Signal zu überprüfen und Fälle nach deinen Wünschen zu verwalten.
Didit · iBeta Level 1 PAD
Didit · Liveness
Didit · Face Search 1:N
Didit · Reusable KYC
Didit · Surfaces
Didit · Methods
$ curl -X POST https://verification.didit.me/v3/session/ \
-H "x-api-key: $DIDIT_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"workflow_id": "wf_proof_of_human",
"vendor_data": "user-42"
}'LIVENESS + FACE_MATCH in einer einzigen Erfassung aus.Dokumentation →$ curl https://verification.didit.me/v3/session/<id>/decision/ \
-H "x-api-key: $DIDIT_API_KEY"
# Sample verdict
{
"status": "Approved",
"liveness": { "score": 96 }
}X-Signature-V2 im signierten Webhook.Dokumentation →You are integrating Didit's Proof of Human gate into <my_stack>. Block AI agents, deepfakes, masks, and duplicate accounts from human-only surfaces — signup, voting, contests, marketplaces, dating. Two endpoints, one verdict.
1. Prove a real human is present (Liveness). ONE call to the Sessions API runs a Presentation Attack Detection (PAD)-certified passive selfie and returns a verdict in sub-2-seconds.
2. Prove the human is unique (Face Search 1:N). Same Sessions API workflow runs Face Search 1:N against your account's private face index.
Pricing (public):
- Passive Liveness: $0.10 per check
- Active 3D Liveness: $0.15 per check (motion challenge — use for high-sensitivity flows)
- Face Search 1:N: free, included
- First 500 verifications free every month, forever
PRE-REQUISITES
- Production API key from https://business.didit.me (sandbox key in 60s, no card).
- Webhook endpoint with Hash-based Message Authentication Code (HMAC) SHA-256 verification using the X-Signature-V2 header.
- A workflow_id from the Workflow Builder that contains the LIVENESS feature, and (recommended) FACE_MATCH and IP_ANALYSIS so Sybil dedupe + agent / bot signals come in on the same verdict.
STEP 1 — Build the Proof of Human workflow once
POST https://verification.didit.me/v3/workflows/
Headers:
x-api-key: <your api key>
Content-Type: application/json
Body:
{
"workflow_label": "proof_of_human",
"features": [
{ "feature": "LIVENESS", "config": { "method": "PASSIVE" } },
{ "feature": "FACE_MATCH" },
{ "feature": "IP_ANALYSIS" }
],
"face_liveness_score_decline_threshold": 30
}
Notes:
- LIVENESS, FACE_MATCH, IP_ANALYSIS are exact, case-sensitive feature names.
- method enum: PASSIVE (one frame) · FLASHING (3D flash) · ACTIVE_3D (action + flash). Use ACTIVE_3D for the highest-sensitivity surfaces (large-value account creation, voting, contest finals).
STEP 2 — Open a Proof of Human session per user
POST https://verification.didit.me/v3/session/
Headers:
x-api-key: <your api key>
Content-Type: application/json
Body:
{
"workflow_id": "<the workflow_id from step 1>",
"vendor_data": "<your internal user id>",
"callback": "https://<your-app>/proof-of-human/callback",
"metadata": {
"surface": "<signup | vote | contest | marketplace | dating>"
}
}
Response: 201 Created with the hosted session_url. Redirect the user. The hosted UI opens the front camera, captures one passive frame (or a short motion challenge for ACTIVE_3D), runs Liveness + Face Search 1:N, returns the verdict in sub-2-seconds.
STEP 3 — Read the signed verdict on the webhook
Body (excerpted for a clean human):
{
"session_id": "<uuid>",
"vendor_data": "<your user id>",
"status": "Approved",
"liveness": {
"status": "Approved",
"method": "PASSIVE",
"score": 96,
"warnings": []
},
"face": {
"status": "Approved",
"similarity_score": null,
"matches": []
},
"ip_analysis": { "status": "Approved" }
}
Body (excerpted for a duplicate):
{
"status": "In Review",
"liveness": { "status": "Approved", "score": 94 },
"face": {
"status": "In Review",
"matches": [
{ "vendor_data": "user_8124", "similarity_score": 0.97 }
],
"warnings": [{ "code": "POSSIBLE_DUPLICATED_FACE" }]
}
}
Verify X-Signature-V2 BEFORE trusting the body — HMAC SHA-256 of the raw bytes with your webhook secret.
Session status enum (exact case): Approved | Declined | In Review | Resubmitted | Expired | Not Finished | Kyc Expired | Abandoned.
Liveness warning catalog:
- LIVENESS_FACE_ATTACK PAD attack suspected (print / replay / mask / GAN)
- LOW_LIVENESS_SCORE score below threshold
- NO_FACE_DETECTED no face in the capture
- AGE_NOT_DETECTED capture quality too low for age signal
- POSSIBLE_DUPLICATED_FACE same face previously verified on your account
STEP 4 — Branch your surface on the final verdict
Approved → grant access to the human-only surface.
Declined → block; log the rejected agent / spoof attempt.
In Review → hold; show a review-pending banner, route to ops queue.
Not Finished → user abandoned; safe to re-prompt.
STEP 5 — Alternate path (server-to-server, when you have the selfie)
POST https://verification.didit.me/v3/passive-liveness/
Headers:
x-api-key: <your api key>
Body (multipart/form-data):
image <single front-camera selfie>
Then dedupe:
POST https://verification.didit.me/v3/face-search/
Body (multipart/form-data):
image <same selfie>
vendor_data <your user id>
Use the standalone path for native onboarding apps that capture the selfie locally. Active 3D liveness REQUIRES the hosted session — it needs the motion challenge to run.
CONSTRAINTS
- Base URL for /v3/* endpoints is verification.didit.me (NOT apx.didit.me).
- Feature enum is UPPERCASE: LIVENESS, FACE_MATCH, IP_ANALYSIS, ID_VERIFICATION, AML, AGE_ESTIMATION.
- Method enum is UPPERCASE: PASSIVE, FLASHING, ACTIVE_3D.
- Auth header is x-api-key (lowercase, hyphenated).
- Webhook signature header is X-Signature-V2 (NOT X-Signature).
- Status casing matches exactly: Approved, Declined, In Review, Expired, Not Finished, Resubmitted, Kyc Expired, Abandoned.
- 200+ fraud signals are evaluated on every session at no extra cost.
PRO TIP
- Bind a Reusable Credential to each approved user. The next Didit-powered surface that needs the same gate consumes the credential at zero cost — the Proof of Human "compounds" across the network.
Read the docs:
- https://docs.didit.me/core-technology/liveness/overview
- https://docs.didit.me/core-technology/face-search/overview
- https://docs.didit.me/sessions-api/create-session
- https://docs.didit.me/integration/webhooks
Start free at https://business.didit.me — sandbox key in 60 seconds, 500 verifications free every month, no credit card.$0 / Monat. Keine Kreditkarte erforderlich.
Zahle nur, was du nutzt. Über 25 Module. Öffentliche Preise pro Modul, keine monatliche Mindestgebühr.
Individuelle MSA & SLA. Für große Volumina und regulierte Programme.
Kostenlos starten → nur zahlen, wenn eine Prüfung läuft → Enterprise für einen individuellen Vertrag, SLA oder Datenresidenz freischalten.
Didit is infrastructure for identity and fraud, the platform we wished existed when we were building products ourselves: open, flexible, and developer-friendly, so it works as a real part of your stack instead of a black box you integrate around.
One API covers verifying people (KYC, know your customer), verifying businesses (KYB, know your business), screening crypto wallets (KYT, know your transaction), and monitoring transactions in real time, on a stack built to be:
The footprint underneath: 14,000+ document types in 48+ languages, 1,000+ data sources, and 200+ fraud signals on every session. The Didit infrastructure dynamically learns from every session and gets better every day.
A short challenge that proves three things at once:
The first two are called liveness and Presentation Attack Detection (PAD). The third is 1:N face deduplication. Together they're what most platforms now mean when they say "proof of human".
Two reasons.
The full flow normally takes under 30 seconds end-to-end, pick up the ID, snap the document, snap the selfie, done. That is the fastest in the market. Legacy KYC providers usually take more than 90 seconds for the same flow.
On the back end, Didit returns the result in under two seconds at p99, measured from the moment the user finishes the selfie to the moment your webhook fires. Mobile capture is tuned for slow phones and slow networks: progressive image compression, lazy software development kit load, and a one-tap hand-off from desktop to phone via QR code if the user starts on web.
The model is trained on real captures vs. every known spoof class, print, screen replay, mask, morph, Generative Adversarial Network (GAN) portrait, diffusion render. Real captures carry tiny artefacts no spoof has: micro-motion of the head, sub-pixel skin texture, reflections that match the device's flash spectrum, depth cues from a single passive frame.
The iBeta Level 1 PAD certification, the bar the US National Institute of Standards and Technology cites, measures exactly this performance, and Didit holds it across every passive-liveness session.
Every session lands on one of seven clear statuses, so your code always knows what to do:
Approved, every check passed. Move the user forward.Declined, one or more checks failed. You can allow the user to resubmit the specific failed step (for example, re-take the selfie) without re-running the whole flow.In Review, flagged for compliance review. Open the case in the console, see every signal, decide approve or decline.In Progress, user is mid-flow.Not Started, link sent, user has not opened it yet. Send a reminder if it sits too long.Abandoned, user opened the link but did not finish in time. Re-engage or expire.Expired, the session link aged out. Create a new session.A signed webhook fires on every status change, so your database always stays in sync. Abandoned and declined sessions are free.
Production data is processed and stored in the European Union by default, on Amazon Web Services. Enterprise contracts can request alternative regions for jurisdictions whose regulators require it.
Encryption everywhere. AES-256 at rest across every database, object store, and backup. Transport Layer Security 1.3 in transit on every API call, webhook, and Business Console session. Biometric data is encrypted under a separate Customer Master Key.
Retention is yours to control. Default retention is indefinite (unlimited) unless you configure shorter, between 30 days and 10 years per application, and you can delete any individual session at any time from the dashboard or the API.
Certifications: SOC 2 Type 1 (Type 2 audit in progress), ISO/IEC 27001:2022, iBeta Level 1 PAD, and a public attestation from Spain''s Tesoro / SEPBLAC / CNMV that Didit''s remote identity verification is safer than verifying someone in person. Full report at /security-compliance.
Didit ships compliant by default for the regulators that matter to identity infrastructure:
Detailed memo, every certificate, every regulator letter: /security-compliance.
Three integration paths, pick whichever fits your stack:
Same dashboard, same billing, same pay-per-success price for all three. Step-by-step guide at docs.didit.me/integration/integration-prompt.
Two paths.
POST /v3/session/ with the workflow_id you built in the Business Console. We host the capture UI, run Liveness + Face Search 1:N, return a signed verdict to your webhook. Five-minute integration.POST /v3/passive-liveness/ with the selfie you already captured locally, then POST /v3/face-search/ for Sybil dedupe.Full cURL example, response shape, and the agent-pastable prompt are above. The Model Context Protocol (MCP) server speaks both surfaces to Claude, Cursor, and any other agent.
Data minimisation by default. The selfie is processed in memory; the verdict, liveness score, and an irreversible face template are persisted; the raw image is deleted unless retention is explicitly enabled. The face template is a one-way hash, you cannot reconstruct the underlying face from it.
We never sell, share, or train third-party models on a customer's biometric data. The full data-processing terms live at didit.me/terms/business; the privacy notice the end user sees is at didit.me/terms/verification-privacy-notice.
The session returns one of four outcomes:
A signed webhook lands at every state change, so you only render against the latest verdict.
Eine API für KYC, KYB, Transaktionsüberwachung und Wallet-Screening. In 5 Minuten integriert.