Face Spoof Detection (BETA)

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

important

This API is available in Enterprise plan only.

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 (BETA) 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 (BETA), 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
}