How to Detect Deepfake Images using JavaScript

Welcome to our in-depth tutorial on detecting deepfakes using AI and JavaScript! As deepfake technology continues to evolve, learning how to identify altered media is more important than ever.

Whether you're involved in cybersecurity, media verification, or just curious about how AI can detect synthetic content, this guide will equip you with the tools and insights needed for effective detection.

We'll walk you through key methods and code examples to help you get started with deepfake detection using JavaScript. Let’s jump right in!

What is Deepfake Detection?

Image deepfake detection involves applying AI and machine learning methods to spot altered or fabricated images created using deepfake technology.

Deepfakes leverage sophisticated algorithms to produce fake images that often appear strikingly realistic.

Detection techniques focus on identifying visual anomalies, such as irregular lighting, distortions in facial features, or unusual pixel patterns, to assess whether an image has been manipulated.

The primary objective of deepfake detection is to authenticate images and prevent the dissemination of deceptive or harmful content.

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Create Your Eden AI Account

1. Sign Up: If you don’t yet have an Eden AI account, simply sign up for a free account using this link. Once registered, you can obtain your API key from the API Keys section, along with the free credits offered by Eden AI.

2. Access vision Technologies: After logging in, navigate to the vision section of the platform.

3. Select Image Deepfake Detection: Choose deepfake detection.

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Implementing Deepfake Detection in JavaScript

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Install JavaScript’s Axios Module: To interact with the Eden AI API using JavaScript, you’ll need the module for making HTTP requests. Install it via npm:

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Code Example

Once you've set up your JavaScript environment, use the following code to send a request to the Eden AI API for deepfake detection.

const axios = require("axios").default;

const options = {
  method: "POST",
  url: "https://api.edenai.run/v2/image/deepfake_detection",
  headers: {
    Authorization: "Bearer YOUR_API_KEY",
  },
  data: {
    providers: "sightengine",  // AI provider for deepfake detection
    file_url: "🔗 URL of your image",  // Replace with the image URL to check
  },
};

axios
  .request(options)
  .then((response) => {
    console.log(response.data);  // Output the results of the detection
  })
  .catch((error) => {
    console.error(error);  // Handle any errors
  });

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• ‍Authorization Header: The API key is placed in the header to authenticate the request.‍
• Request URL: The URL provided by Eden AI to send deepfake detection requests.‍
• Payload: The payload contains the file_url (the image URL) and the providers field (AI provider to use).‍
• Sending the Request: The request is sent using axios and the result is logged to the console for inspection.

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Interpreting the Results

Once you send the request, you will receive a JSON response. A typical response could look like this:

{
  "sightengine": {
    "is_fake": true,
    "confidence": 95.6
  }
}

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Why Eden AI is the Best Tool for Deepfake Detection

Eden AI offers a robust and flexible deepfake detection solution with several advantages.

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Multiple Provider Options

‍Eden AI allows you to choose from a variety of AI providers, giving you flexibility in detecting deepfakes using different models.

High Accuracy

‍The deepfake detection models used by Eden AI are trained on vast datasets and designed to deliver reliable results with high accuracy.

Ease of Integration

‍With simple and easy-to-understand code examples in JavaScript, Eden AI makes it easy to integrate deepfake detection into your applications.

Scalability

‍Eden AI can handle large volumes of requests, making it a great choice for applications that need to process numerous images in real-time.

Security and Trust

‍The Eden AI platform prioritizes data security and privacy, ensuring that your image data is handled securely.

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Next step in your project

The Eden AI team can help you with your Image Deepfake Detection integration project. This can be done by :

• Organizing a product demo and a discussion to understand your needs better.
• By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs
• Having the possibility to integrate on a third-party platform: we can quickly develop connectors.

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