ScamShield is a high-end forensic investigation tool designed to detect and analyze AI-driven job recruitment scams. It combines advanced NLP classification, real-time salary calibration, and deep-header e-mail auditing into a clinical, minimalist interface.
- /fries-rishi-main/frontend: React (Vite) application with Tailwind CSS and Framer Motion.
- /scammmmm-final-backend-main/scammmmm-final-backend-main: FastAPI backend with ML scoring logic.
The backend handles the forensic analysis scoring and ML classification.
Prerequisites: Python 3.8+
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Navigate to the backend directory:
cd scammmmm-final-backend-main/scammmmm-final-backend-main -
(Optional) Create a virtual environment:
python -m venv venv source venv/bin/scripts/activate # On Windows: venv\Scripts\activate
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Install dependencies:
pip install -r req.txt
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Run the server:
python -m uvicorn main:app --reload --port 8000
The API will be live at
http://localhost:8000
The frontend provides the clinical forensic dashboard and real-time chat interface.
Prerequisites: Node.js 18+
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Navigate to the frontend directory:
cd fries-rishi-main/frontend -
Install dependencies:
npm install
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Run the development server:
npm run dev
The interface will be live at
http://localhost:5173
- Job URL Investigation: Analyzes job postings for registration fees, infrastructure markers, and domain hygiene.
- E-mail Audit Tool: Parses suspicious recruiter emails for urgency tactics and fraud signals.
- Salary Calibration: Cross-checks compensation against market rates to detect bait-and-switch scams.
- Company Verification: Cross-references recruitment firms against fraudulent naming conventions.
- Frontend: React, Vite, Framer Motion, Tailwind CSS, Lucide Icons.
- Backend: FastAPI, Scikit-learn, Joblib, Uvicorn.
- Analysis: Custom NLP classification for scam pattern detection.
Developed for ScamShield Forensic Labs.