Multi-Modal AI Content & Deepfake Detection MVP

RealityCheck is a unified AI-based platform that detects synthetic or manipulated content across text, images, audio, and video using state-of-the-art machine learning models.

As generative AI content becomes more prevalent, distinguishing between real and AI-generated media is increasingly critical. RealityCheck addresses this by offering a modular, multi-modal deepfake detection system for:

• 📝 Text – Detection of AI-generated writing
• 🖼️ Images – Classification of deepfake and synthetic visuals
• 🔊 Audio – Detection of voice cloning or audio synthesis
• 🎥 Video – Frame-wise analysis to identify deepfake manipulations

Accessible via an intuitive Streamlit interface, RealityCheck is designed as a proof of concept (MVP) for fact-checking tools, journalists, forensics experts, and the general public.

👉 Launch the App No installation required — works directly in your browser.

✅ Real-time predictions ✅ Lightweight and fast model inferences ✅ Dynamic upload interface per modality ✅ Visual confidence feedback with result labeling ✅ Deployed and shareable via Streamlit Cloud

Frontend:

• Streamlit for UI/UX
• Pillow for image handling

ML/Inference:

• Transformers by Hugging Face
• PyTorch & torchaudio
• OpenCV for video frame extraction
• librosa for audio analysis

Video & Media:

• moviepy, ffmpeg-python for video handling
• opencv-python-headless for server-safe image/video operations

# Clone the repo
git clone https://github.com/yourusername/realitycheck.git
cd realitycheck

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run app.py

realitycheck/
├── app.py                       # Streamlit UI
├── text_detector.py            # Text analysis logic
├── image_detector.py           # Image classification model
├── audio_detector.py           # Audio feature analysis
├── video_detector.py           # Frame sampling and video analysis
├── requirements.txt
└── README.md

• Replace heuristic audio model with deep learning-based speech detection
• Expand video analysis to support live stream and real-time feeds
• Add explanation features (e.g., Grad-CAM for images, attention maps for text)
• Build API endpoints for integration with third-party tools

Certainly! Here's a professional and concise "🤝 Collaboration" section you can include in your README:

We welcome contributions from researchers, developers, and domain experts passionate about AI safety and media integrity. Whether it's improving model performance, enhancing UI/UX, or integrating new detection techniques — your expertise can help evolve RealityCheck into a powerful open-source tool.

Feel free to fork the repo, open issues, or submit pull requests!

This project is licensed under the MIT License. See the LICENSE file for more details.