🛡️ Shodh AI: Misinformation & Deepfake Detector

Shodh AI is a unified security dashboard that detects digital manipulation in Video, Audio, and Images, and verifies spoken content against global news sources.

🚀 Features

• Visual Deepfake Detection — EfficientNetV2-S model (fine-tuned on FaceForensics++) identifies face manipulation in images and videos.
• Audio Authenticity Scan — Detects synthetic/AI-generated voices using Audio Spectrogram Transformers.
• Speech-to-Text Transcription — Powered by OpenAI Whisper for high-accuracy script extraction.
• Editable Transcript Verification — Review and correct the AI transcript before fact-checking.
• News Fact-Checker — Cross-references transcripts with Google News RSS to calculate a truth/correlation score.
• Unified Interface — One upload. One click. Full analysis.

🏛️ Team & Core Contributions

Contributor	Core Development Role	Technical Focus Area
Bhanu Partap Singh Rajora	Core Deep Learning Researcher	NLP layer orchestration, assertion extraction models, and semantic similarity scoring
Ankit Thakur	Data Pipeline Engineer	Preprocessing workflows, asynchronous FastAPI backend infrastructure, and Java bytecode static analysis data streams
Devansh Sharma	Evaluation & Systems Engineer	Fact-checking metric validation, hallucination profiling, and benchmark dataset integration

• Affiliation: Maharaja Agrasen Institute Of Technology
• Team Name: DELVYONS

🛠️ Local Installation

1. Clone the Repository

git clone https://github.com/YOUR_USERNAME/Misinformation-detector.git
cd Misinformation-detector

2. Create Virtual Environment

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Dashboard

streamlit run shodh_dashboard.py

☁️ Streamlit Cloud Deployment

This project is ready for one-click deployment on Streamlit Cloud:

1. Push your repo to GitHub.
2. Go to share.streamlit.io and sign in with GitHub.
3. Click New app → Select your repo → Set Main file to shodh_dashboard.py.
4. Click Deploy.

The packages.txt file automatically installs system dependencies (ffmpeg, libsndfile) on the cloud server.

📦 Project Structure

File	Description
shodh_dashboard.py	Main Streamlit application (cloud-ready)
scanner_app.py	Desktop auto-scanner (PyQt6, local use only)
weight.pth	Trained deepfake detection weights (~80 MB)
blaze_face_short_range.tflite	MediaPipe face detection model
requirements.txt	Python dependencies
packages.txt	System-level apt dependencies for Streamlit Cloud
.streamlit/config.toml	Streamlit theme and server configuration

🛡️ Ethics & Disclaimer

This tool is intended for research and educational purposes. Always verify AI results with official sources.