https://github.com/somyadipghosh/fakebuster
FakeBuster is a powerful tool designed to detect and expose deepfakes, misinformation, and digital deception.
https://github.com/somyadipghosh/fakebuster
api-rest css html javascript model-deployment python
Last synced: 10 months ago
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FakeBuster is a powerful tool designed to detect and expose deepfakes, misinformation, and digital deception.
- Host: GitHub
- URL: https://github.com/somyadipghosh/fakebuster
- Owner: somyadipghosh
- Created: 2025-04-18T14:43:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-06T06:02:11.000Z (about 1 year ago)
- Last Synced: 2025-05-06T07:26:48.881Z (about 1 year ago)
- Topics: api-rest, css, html, javascript, model-deployment, python
- Language: JavaScript
- Homepage: https://somyadipghosh.is-a.dev/fakebuster/home.html
- Size: 1.3 MB
- Stars: 1
- Watchers: 1
- Forks: 3
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# 🕵♂ Project: FakeBuster – Real-Time Deepfake & Fake News Detector
## 🚀 Overview
FakeBuster is a browser extension and web tool that detects deepfakes and misinformation in real-time. Users can scan videos, images, or news articles as they browse online and get instant feedback on whether the content is manipulated or fake.
---
## 🧰 Tech Stack
### Frontend:
- React.js (SPA)
- Tailwind CSS (Styling)
- JavaScript
### Backend:
- Python (Flask or FastAPI)
- Hugging Face Transformers (Fake News Detection)
- Deepfake Detection API (e.g., Deepware)
- MongoDB (Optional for saving scan history)
### APIs Used:
1. Fake News Detection API (RapidAPI)
2. Fake News Detector API REST (DistilBERT Model)
3. Falah/News_Detection (Hugging Face Model)
4. Media Bias/Fact Check API
5. Deepware Scanner API (for deepfake detection)
---
## 🧱 Features
### 1. Deepfake Detection
- Upload image or video URL
- Backend sends data to Deepware API
- Response includes probability of manipulation
### 2. Fake News Detection
- Paste article text or URL
- Text is tokenized and passed to a pre-trained DistilBERT model (or API)
- Returns verdict: Real, Fake, or Biased
### 3. Deepfake Image Detection
- Upload an image for verification
- Backend sends data to Deepware API
- Response includes probability of image manipulation
### 4. Deepfake Video Detection
- Upload a video or paste a video URL
- Backend extracts keyframes and analyzes them via API
- Returns deepfake probability score
---
## 🔧 Step-by-Step Implementation
### Phase 1: Backend Setup
1. Initialize Flask or FastAPI project
2. Implement /detect-deepfake endpoint using Deepware API
3. Implement /check-news endpoint using HuggingFace or RapidAPI model
4. Enable CORS for cross-origin requests
### Phase 2: Frontend Web Tool
1. Set up React project with Tailwind CSS
2. Build UI pages:
- Home (with scan buttons)
- News Check
- Image/Video Upload
3. Use Axios to call Flask APIs
### Phase 3: Testing
1. Use FaceForensics++ samples for deepfake tests
2. Use LIAR/FakeNewsNet dataset articles for NLP validation
3. Test Chrome extension on news sites and social media
### Phase 4: Deployment (Optional)
- Host backend on Render or Railway
- Host frontend on Vercel
- Load extension in Chrome (Developer Mode)
---
## 🌟 Optional Features
- Real-time feed monitoring for Twitter or YouTube
- User dashboard for scan history
- Voice deepfake detection
- Gamified "Spot The Fake" mode for awareness
---
## 📌 Notes
- All APIs mentioned provide free tiers or open-source models
- Use Tailwind for responsive, clean UI
- Follow Chrome Extension v3 security guidelines (e.g., avoid eval)
---