A high-precision AI skill for detecting physical presence and preventing facial spoofing from a single selfie image.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install didit-liveness-detection
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install didit-liveness-detection using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Didit Passive Liveness Detection?
This Openclaw Skills integration provides a robust solution for passive liveness detection using the Didit API. It enables developers to verify that a user is physically present by analyzing a single image without requiring complex movements or interactions. By leveraging advanced computer vision, this skill identifies presentation attacks like printed photos, digital screens, and deepfakes with 99.9% accuracy.
Built for high-security environments, the skill analyzes face quality, luminance, and biometric markers to ensure the integrity of the authentication process. It is an essential component for any developer using Openclaw Skills to build secure onboarding or identity verification systems that need to mitigate identity fraud and sophisticated spoofing attempts.
Didit Passive Liveness Detection Use Cases
- Preventing account takeover by ensuring a live person is present during login.
- Automating identity verification workflows for financial, healthcare, or restricted access applications.
- Enhancing fraud prevention by detecting masks, screen captures, or printed photo attacks in real-time.
- Integrating seamless, passive biometric checks into mobile or web-based user onboarding flows.
How Didit Passive Liveness Detection Works
- Capture a high-quality selfie image from the user in a supported format such as JPEG, PNG, or WebP.
- Submit the image to the Didit standalone API via the Openclaw Skills interface, optionally setting custom decline thresholds.
- The AI engine performs a single-frame CNN analysis to evaluate liveness markers, face quality, and environmental lighting.
- The system returns a structured JSON response containing the liveness score, approval status, and detailed risk warnings if applicable.
Didit Passive Liveness Detection Setup
To get started with this skill, you must provide a valid API key from the Didit Business Console.
# Set your Didit API key as an environment variable
export DIDIT_API_KEY="your_api_key_here"
If you do not have an account, you can register programmatically by sending a POST request to the Didit registration endpoint, verifying your email via OTP, and retrieving your API key from the response.
Didit Passive Liveness Detection Data Schema & Taxonomy
| Field | Type | Description |
|---|---|---|
status |
string | The final result: "Approved" or "Declined" based on liveness logic. |
score |
integer | A 0-100 confidence value indicating the likelihood of a real person. |
face_quality |
float | Metric evaluating the clarity and position of the face in the image. |
entities |
array | Contains metadata like estimated age, gender, and face bounding box (bbox). |
warnings |
array | Specific risk tags such as NO_FACE_DETECTED or LIVENESS_FACE_ATTACK. |
Didit Passive Liveness Detection Advanced Features
- Configurable liveness score decline thresholds to balance security and user friction.
- Automatic image rotation capabilities to correctly identify faces in varied orientations.
- Sophisticated spoof detection targeting screen captures, masks, and AI-generated deepfakes.
- Support for multi-agent workflows by combining liveness checks with face-match and document verification tools.
- Detailed luminance and quality analysis to provide actionable feedback to users for retaking photos.
SKILL.md
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