Face Spoof Detection

This API verifies if the image is original or spoofed. It returns a boolean value spoof_detected in output.

HTTP Method and Endpoint

POST | POST https://api.videosdk.live/ai/v1/face-verification/detect-spoof

Headers Parameter

Authorization

values : YOUR_TOKEN_WITHOUT_ANY_PREFIX

This will be a JWT token generate using VideoSDK ApiKey and Secret.

Note : the token will not include any prefix such as "Basic " or "Bearer ". Just pass a token as value.

You can generate a new token by refering this Guide: Generate Auth token

Content-Type

values : application/json

This is usefull for json body parameters, so that VideoSDK servers can understand that the incoming body parameter will be a JSON string.

HEADER

NodeJS

• NodeJS
• PHP
• Python

const headers = {
   'Authorization': '<Your_API_TOKEN>',
   'Content-Type': 'application/json'
};

Data Parameter

Base64 Encoding Format

The images should be converted from binary format to a Base64 string

Base64 Encoding

NodeJS

• NodeJS
• PHP
• Python

const base64Data = Buffer.from(image).toString('base64');

Images must be Base64 encoded in the following format:

data:image/jpeg;base64,${Base64data}

Request Format

The request body should contain two Base64-encoded images in the following format:

const data = {
   "img": "data:image/jpeg;base64,${Base64data}", // Base64-encoded image
}

Sending Image Comparison Request

The request is made using the following code:

Axios POST Request

NodeJS

• NodeJS
• PHP
• Python

const response = await axios.post(url, data, { headers });
console.log(response.data);

This sends the encoded images to the specified URL for comparison.

Code Snippet for API Integration

Below is a code snippet demonstrating how to use the Face Spoof Detection API with Node.js:

import axios from 'axios';
import { readFileSync } from 'fs';
import path from 'path';
import dotenv from 'dotenv';

// Load environment variables from .env file
dotenv.config();

// Get the directory of the current file
const __dirname = path.resolve();

// Function to read image files and convert them to base64
function getBase64Image(imageName) {
   const filePath = path.join(__dirname, 'image', imageName);
   console.log(`Resolved file path: ${filePath}`);
   try {
      const image = readFileSync(filePath);
      const base64Data = Buffer.from(image).toString('base64');
      return `data:image/jpeg;base64,${base64Data}`;
   } catch (error) {
      console.error(`Error reading image file: ${filePath}`, error.message);
      throw error;
   }
}

async function spoofDetection(spoofimg) {
   const url = 'https://api.videosdk.live/ai/v1/face-verification/detect-spoof';
   const headers = {
      'Authorization': `${process.env.API_KEY}`,
      'Content-Type': 'application/json'
   };
   const data = {
      img: getBase64Image(spoofimg)
   };

   try {
      const response = await axios.post(url, data, { headers });
      console.log(response.data);
   } catch (error) {
      console.error('Error during spoof detection:', error.response ? error.response.data : error.message);
   }
}

// Main function to run the API calls
async function main() {
   const spoofimg = 'img.jpg';
   await spoofDetection(spoofimg);
}

// Run the main function
main().catch(error => console.error('Error in main function:', error.message));

Given below is a test run of the example.

Input Image :

When this image is sent to the Face Spoof Detection API, the response will return a boolean value spoof_detected in output :

{
   spoof_detected: true // true if spoof detected in image else false
   accuracy: 0.9899068176746368 // accuracy of spoof detection
}