TruthGuard

Winner of the 2023 Congressional App Challenge

AI-powered fake news detector that detects misinformation and bias to combat the spread of misinformation. TruthGuard empowers you to separate fact from fiction and protect the trustworthiness of the information you consume.

Try TruthGuard | Congressional App Challenge Winner

---

Table of Contents

• Features
• Architecture
• Project Structure
• Getting Started
  • Prerequisites
  • Clone the Repository
  • Server Setup
  • Client Setup
  • Chrome Extension Setup
• Model Details

---

Features

• Text Analysis — Paste any article or news text to get a real-time credibility score
• URL Scraping — Enter a URL and TruthGuard automatically extracts and analyzes the article content
• Image OCR — Upload a screenshot of an article and TruthGuard extracts the text using Tesseract.js for analysis
• Chrome Extension — Analyze articles directly from your browser without leaving the page

---

Architecture

TruthGuard is a 3-tier application:

┌──────────────────────────────────────────────────────┐
│                     Frontend                         │
│         React + Vite + Tailwind CSS                  │
│     (Web App & Chrome Extension)                     │
│                                                      │
│  ┌─────────────┐  ┌──────────┐  ┌────────────────┐  │
│  │ Text Input  │  │ URL Input│  │ Image OCR      │  │
│  │             │  │          │  │ (Tesseract.js) │  │
│  └──────┬──────┘  └────┬─────┘  └───────┬────────┘  │
└─────────┼──────────────┼────────────────┼────────────┘
          │              │                │
          ▼              ▼                ▼
┌──────────────────────────────────────────────────────┐
│                    Backend                           │
│              FastAPI + ONNX Runtime                  │
│                                                      │
│  ┌──────────────────┐  ┌──────────────────────────┐  │
│  │ POST /predict    │  │ POST /scrape             │  │
│  │ (classification) │  │ (BeautifulSoup scraping) │  │
│  └──────────────────┘  └──────────────────────────┘  │
└────────────────────────┼─────────────────────────────┘
                         │
                         ▼
┌──────────────────────────────────────────────────────┐
│                     Model                            │
│       DistilBERT-based Binary Classifier             │
│       Quantized ONNX (INT8) for fast inference       │
│       Hosted on Hugging Face Hub                     │
└──────────────────────────────────────────────────────┘

---

Project Structure

TruthGuard/
├── client/                        # Web application (React + Vite)
│   ├── src/
│   │   ├── App.jsx                #   Main application component
│   │   ├── main.jsx               #   Entry point
│   │   └── index.css              #   Global styles (Tailwind)
│   ├── public/                    #   Static assets & icons
│   ├── dist/                      #   Production build output
│   ├── .env                       #   Environment variables
│   ├── vite.config.js             #   Vite configuration
│   └── package.json
│
├── chrome-extension/              # Chrome browser extension
│   ├── src/
│   │   ├── App.jsx                #   Extension popup component
│   │   ├── main.jsx               #   Entry point
│   │   └── index.css              #   Styles
│   ├── manifest.json              #   Chrome extension manifest (v3)
│   ├── dist/                      #   Built extension (ready to load)
│   ├── TruthGuardExtension.zip    #   Packaged extension
│   ├── vite.config.js
│   └── package.json
│
├── server/                        # FastAPI backend
│   ├── main.py                    #   API endpoints (/predict, /scrape)
│   ├── requirements.txt           #   Python dependencies
│   ├── Procfile                   #   Deployment configuration
│   └── LICENSE
│
├── model/                         # ML model & training data
│   ├── final_model/
│   │   └── truth_guard_model.pt   #   Trained PyTorch model
│   ├── onnx_model/
│   │   ├── truth_guard.onnx       #   ONNX model (full precision)
│   │   └── truth_guard_int8.onnx  #   ONNX model (INT8 quantized)
│   ├── kaggle/                    #   Kaggle Fake News dataset
│   ├── liar2/                     #   LIAR-2 dataset
│   └── truth-guard-training.ipynb #   Training notebook
│
└── README.md

---

Getting Started

Clone the Repository

git clone https://github.com/RishabA/TruthGuard.git
cd TruthGuard

Server Setup

cd server

# Install dependencies
pip install -r requirements.txt

# Start the server
uvicorn main:app --reload --port 8000

The API will be available at http://localhost:8000.

Client Setup

cd client

# Install dependencies
npm install

# Configure the server URL
# Edit .env and set: VITE_SERVER_URL=http://localhost:8000

# Start the development server
npm run dev

The web app will be available at http://localhost:5173.

Chrome Extension Setup

cd chrome-extension

# Install dependencies
npm install

# Configure the server URL
# Edit .env and set: VITE_SERVER_URL=http://localhost:8000

# Build the extension
npm run build

Then load the extension in Chrome:

1. Navigate to chrome://extensions/
2. Enable Developer mode (toggle in top-right)
3. Click Load unpacked
4. Select the chrome-extension/dist/ folder

---

Model Details

TruthGuard uses a DistilBERT-based binary classifier trained to distinguish between real and fake news articles.

Property	Value
Base Tokenizer	distilbert-base-uncased
Architecture	Transformer Encoder + Classification Head
Training Framework	PyTorch
Inference Runtime	ONNX Runtime (CPU)
Quantization	INT8 (for production)
Max Input Length	512 tokens
Output	Sigmoid probability (0.0 = Real, 1.0 = Fake)
Training Data	Kaggle Fake News Dataset, LIAR-2 Dataset
Model Hosting	Hugging Face Hub

The training process is documented in model/truth-guard-training.ipynb.

---

API Endpoints

Method	Endpoint	Description	Request Body
POST	/predict	Classify text as real or fake news	{ "text": "..." }
POST	/scrape	Scrape article content from a URL	{ "url": "..." }