🚨 TrueAD - AI-Powered Scam Advertisement Detection

TrueAD is an AI-powered Flask-based web application that collects, analyzes, and detects scam advertisements on social media platforms in real-time using web scraping, NLP, computer vision, and machine learning.

🔧 Tech Stack

• Backend: Flask (Python)
• Web Scraping: BeautifulSoup, Requests
• NLP & ML: spaCy, BERT, XGBoost
• Image Processing: Tesseract, OpenCV
• Frontend: Tailwind CSS, HTML, JavaScript
• Database: MongoDB or SQLite
• Alerts: Email, Telegram, WebSockets

⚙️ Features

• Real-time scam ad detection from Facebook, Twitter, and Instagram.
• Multi-layered scam detection using ML, NLP, OCR, URL checks, and wallet blacklist verification.
• Real-time alerts sent via email, Telegram, or browser notifications.
• Admin Dashboard to manage ads and view flagged reports.
• AI-powered text and image processing for enhanced scam detection.
• Modular architecture for easy scalability and maintenance.

🛠️ Setup Instructions

1. 📦 Prerequisites

Ensure you have the following installed:

• Python 3.8+
• MongoDB (or SQLite for local setup)
• pip (Python package manager)

2. 🔑 Configuration

Create or update the config.py file with the following structure:

class Config:
    SECRET_KEY = "your_secret_key"
    MONGO_URI = "mongodb://localhost:27017/"
    DEBUG = True
    REDDIT_CLIENT_ID = "your_client_id"
    REDDIT_CLIENT_SECRET = "your_client_secret"
    REDDIT_USERNAME = "your_reddit_username"
    REDDIT_PASSWORD = "your_reddit_password"
    REDDIT_USER_AGENT = "TrueADScraper/1.0"

3. 📥 Install Dependencies

Install the required dependencies:

pip install -r requirements.txt

If requirements.txt is missing, install manually:

pip install flask beautifulsoup4 requests spacy xgboost opencv-python

🚀 Running the App

Use two terminal tabs:

• Terminal 1: Start Flask Backend

python main.py
    

• Terminal 2: Start Social Media Scraping and AI Monitoring

python scraper.py
    

Open your browser and visit: http://localhost:5000

📊 Risk Scoring Breakdown

Detection Technique	Weight Added
ML Model Prediction	+0.6
Keyword Match	+0.5
Phishing URL Detected	+0.6
Crypto Wallet Blacklist Match	+0.7
NLP Embedding Match (Semantic)	+0.2
OCR-based Scam Text Detection	+0.3

Messages are flagged if the total risk score > 0.4 (default threshold).

🙌 Contribution Guidelines

🔧 Want to Contribute?

We welcome contributions from developers!

📌 Here's how you can help:

• Improve scam detection accuracy (enhance ML models or NLP logic).
• Add support for more scam patterns or keywords.
• Refactor code for modularity and scalability.
• UI/UX improvements for the dashboard.
• Write documentation or create test cases.

🛠️ To Contribute:

1. Fork this repository.
2. Create a new branch (feature/your-feature-name).
3. Commit your changes with clear messages.
4. Push to your branch and create a Pull Request.

🧠 Future Enhancements

• Admin login & access control.
• Visual scam heatmaps & statistics.
• PDF export of scam reports.
• Multilingual support.
• Integration with Discord, WhatsApp, etc.

📬 Contact

For support, questions, or collaboration:

• 📧 Email: [mukeshkumar.cse24@gmail.com]

📄 License

This project is licensed under the MIT License.