Didit Passive Liveness API

Overview

Verifies that a user is physically present by analyzing a single captured image — no explicit movement or interaction required.

Key constraints:

• Supported formats: JPEG, PNG, WebP, TIFF
• Maximum file size: 5MB
• Image must contain exactly one clearly visible face
• Original real-time photo only (no screenshots or printed photos)

Accuracy: 99.9% liveness detection accuracy, <0.1% false acceptance rate (FAR).

Capabilities: Liveness scoring, face quality assessment, luminance analysis, age/gender estimation, spoof detection (screen captures, printed copies, masks, deepfakes), duplicate face detection across sessions, blocklist matching.

Liveness methods: This standalone endpoint uses PASSIVE method (single-frame CNN). Workflow mode also supports ACTIVE_3D (action + flash, highest security) and FLASHING (3D flash, high security).

API Reference: https://docs.didit.me/standalone-apis/passive-liveness