什么是 Didit 静默活体检测？

Didit 静默活体检测技能使 AI 代理能够通过分析捕获的单张图像来验证用户是否亲自到场。与主动式方法不同，该技术不需要任何动作或交互，在保持 99.9% 准确率的同时提供无缝的用户体验。它是开发者在 Openclaw Skills 生态系统中构建安全身份验证流程的关键组件。

该技能可检测各种演示攻击，包括屏幕截图、打印照片、面具和深度伪造。通过将其集成到您的工作流程中，您可以确保与您的应用程序交互的是真实人类，在不增加用户旅程摩擦的情况下，显著降低欺诈和未经授权访问的风险。

下载入口:https://github.com/openclaw/skills/tree/main/skills/rosasalberto/didit-passive-liveness

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

Didit 静默活体检测 应用场景

• 在用户注册或高额交易期间防止欺诈。
• 实时检测深度伪造和演示攻击。
• 实施无需用户配合动作的无摩擦防伪检查。
• 验证面部质量和亮度，以获得更好的身份证明文件。
• 通过 Openclaw Skills 将身份验证集成到自动化代理工作流中。

1. 代理捕获或接收 JPEG、PNG 或 WebP 等支持格式的用户自拍照。
2. 图像通过 DIDIT_API_KEY 进行身份验证并传输到 Didit 独立 API。
3. AI 使用单帧 CNN（静默方法）分析单帧图像以验证人类存在。
4. 系统计算活体评分，评估面部质量，并进行亮度分析。
5. 返回最终状态（批准或拒绝）以及年龄、性别和风险警告等元数据。

Didit 静默活体检测 配置指南

要在您的 Openclaw Skills 集合中使用此功能，您必须首先从 Didit 业务控制台获取 API 密钥。获取后，按如下方式配置您的环境：

export DIDIT_API_KEY="your_api_key_here"

然后，您可以使用提供的实用脚本启动活体检查：

python scripts/check_liveness.py selfie.jpg --threshold 80

Didit 静默活体检测 数据架构与分类体系

该技能返回详细说明活体评估的结构化 JSON 响应。数据组织如下：

name: didit-passive-liveness
description: >
  Integrate Didit Passive Liveness standalone API to verify a user is physically present.
  Use when the user wants to check liveness, detect spoofing attempts, verify real person
  presence, implement anti-spoofing, or perform passive liveness detection using Didit.
  Analyzes a single image without user interaction. Returns liveness score, face quality,
  and luminance metrics. Detects presentation attacks and duplicate faces.
version: 1.2.0
metadata:
  openclaw:
    requires:
      env:
        - DIDIT_API_KEY
    primaryEnv: DIDIT_API_KEY
    emoji: "??"
    homepage: https://docs.didit.me

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/reference/passive-liveness-api

Authentication

All requests require x-api-key header. Get your key from Didit Business Console → API & Webhooks.

Endpoint

POST https://verification.didit.me/v3/passive-liveness/

Headers

Request Parameters (multipart/form-data)

Example

import requests

response = requests.post(
    "https://verification.didit.me/v3/passive-liveness/",
    headers={"x-api-key": "YOUR_API_KEY"},
    files={"user_image": ("selfie.jpg", open("selfie.jpg", "rb"), "image/jpeg")},
    data={"face_liveness_score_decline_threshold": "80"},
)

const formData = new FormData();
formData.append("user_image", selfieFile);
formData.append("face_liveness_score_decline_threshold", "80");

const response = await fetch("https://verification.didit.me/v3/passive-liveness/", {
  method: "POST",
  headers: { "x-api-key": "YOUR_API_KEY" },
  body: formData,
});

Response (200 OK)

{
  "request_id": "a1b2c3d4-...",
  "liveness": {
    "status": "Approved",
    "method": "PASSIVE",
    "score": 95,
    "user_image": {
      "entities": [
        {"age": 22.16, "bbox": [156, 234, 679, 898], "confidence": 0.717, "gender": "male"}
      ],
      "best_angle": 0
    },
    "warnings": [],
    "face_quality": 85.0,
    "face_luminance": 50.0
  },
  "created_at": "2025-05-01T13:11:07.977806Z"
}

Status Values & Handling

Error Responses

Response Field Reference

Warning Tags

Auto-Decline (always)

Configurable (Decline / Review / Approve)

Common Workflows

Basic Liveness Check

1. Capture user selfie
2. POST /v3/passive-liveness/ → {"user_image": selfie}
3. If "Approved" → user is real, proceed
   If "Declined" → check warnings:
     - NO_FACE_DETECTED → ask user to retake with face clearly visible
     - LOW_FACE_QUALITY → ask for better lighting/positioning
     - LIVENESS_FACE_ATTACK → flag as potential fraud

Liveness + Face Match (combined)

1. POST /v3/passive-liveness/ → verify user is real
2. If Approved → POST /v3/face-match/ → compare selfie to ID photo
3. Both Approved → identity verified

Utility Scripts

export DIDIT_API_KEY="your_api_key"

python scripts/check_liveness.py selfie.jpg
python scripts/check_liveness.py selfie.jpg --threshold 80