skills.rest
didit-protocol
didit-protocolOFFICIAL
Explore all skills by this author →
Website𝕏@getdiditGitHub

didit-liveness-detection

Detect liveness from a single selfie via the Didit standalone API.

23|4|Updated Feb 14, 2026
Search Tags:#biometrics#identity verification#didit#anti-spoofing#liveness detection#selfie verification
One-click install
npx skills add https://github.com/didit-protocol/skills --skill didit-liveness-detection
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: didit-liveness-detection
Source: https://github.com/didit-protocol/skills/tree/main/skills/didit-liveness-detection
Command: npx skills add https://github.com/didit-protocol/skills --skill didit-liveness-detection

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill ensures that the person interacting with your service is a real, physically present individual, preventing fraudulent activities like spoofing or impersonation.

Core Features & Use Cases

  • Passive Liveness Detection: Analyzes a single selfie to determine if it's from a live person or a spoof attempt (print, screen, mask).
  • Accuracy: Boasts 99.9% liveness detection accuracy with a very low false acceptance rate.
  • Use Case: Integrate this into your onboarding flow to confirm the user submitting an ID document is the same person in real-time, preventing account takeovers.

Quick Start

Use the didit-liveness-detection skill to check if the image 'selfie.jpg' is from a live person.

Frequently Asked Questions about didit-liveness-detection

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect liveness from a selfie image to prevent spoofing?▼

Passive liveness detection analyzes a single selfie image to determine physical presence, preventing presentation attacks like printed photos, screen replays, or masks during identity verification.

What do I need to set up to use the Didit liveness detection API?▼

You need a Didit API key for authentication and the Python requests library to submit your selfie image as multipart/form-data to the standalone liveness detection endpoint.

How accurate is passive selfie verification for anti-spoofing?▼

Passive selfie verification provides 99.9% liveness detection accuracy with a very low false acceptance rate, ensuring robust protection against presentation attacks and account takeovers.

How do I integrate liveness checks into a user onboarding flow?▼

Integrate liveness checks by capturing a user selfie during onboarding and submitting it to the Didit API to confirm physical presence and prevent fraudulent account creation in real-time.

Does the didit-liveness-detection Skill work without an ID document?▼

Yes, the didit-liveness-detection Skill operates as a standalone API that detects liveness from a single selfie image, independent of any ID document submission.

Related Skills

Explore More Legal & Compliance Skills→
didit-protocoldidit-protocol
23

didit-passive-liveness

Analyze a single image with the Didit Passive Liveness API to verify user presence.

Official
Intermediate
open-industry-operating-modelopen-industry-operating-model

kyc-customer-onboarding

Automate KYC customer onboarding for commercial banking workflows.

Official
Intermediate
didit-protocoldidit-protocol
23

didit-kyc-onboarding

Orchestrates KYC identity verification workflows and retrieves final decisions for onboarding.

Official
Advanced
didit-protocoldidit-protocol
23

didit-age-estimation

Estimates age from a facial image using Didit's API with configurable thresholds and liveness detection.

Official
Advanced