🔍 PixelTruth — AI-Powered Deepfake Detector

PixelTruth is an AI-powered deepfake detection system for social media images. It uses a custom Convolutional Neural Network (CNN) with advanced preprocessing to classify images as real or AI-generated with 95% accuracy.

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📌 Table of Contents

• About
• Features
• Tech Stack
• Performance
• Installation
• Usage
• Project Structure
• Contributing
• GSSoC 2026
• License

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🧠 About

With the rise of AI-generated media, detecting deepfakes has become critical for media integrity and combating misinformation. PixelTruth addresses this by providing a fast, accurate, and accessible deepfake detection tool built on deep learning.

It analyzes visual artifacts introduced during image synthesis and classifies images in real-time through an intuitive web dashboard.

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✨ Features

• 🖼️ Real-time image analysis — supports JPG, PNG, WebP formats
• 📊 Confidence scores — shows probability of real vs. fake
• 🎨 Modern Streamlit dashboard — glassmorphism UI with visual feedback
• ⚡ Fast inference — lightweight model optimized for speed
• 🔬 Custom preprocessing pipeline — enhanced artifact detection

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🛠️ Tech Stack

Tool	Purpose
Python	Core language
TensorFlow / Keras	CNN model training & inference
OpenCV	Image preprocessing
Streamlit	Web dashboard
NumPy / Matplotlib	Data handling & visualization

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📊 Performance

Metric	Value
Accuracy	95%
Input Size	96 x 96 px
Supported Formats	JPG, PNG, WebP

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⚙️ Installation

⚠️ Prerequisites:

• Python Version: This project strictly requires Python 3.11. (Newer versions like Python 3.12 are currently incompatible with the required TensorFlow dependencies).

• Windows Users:
  • You must have the Microsoft Visual C++ Redistributable installed to run TensorFlow.
  • If the app crashes on launch with a DLL error, install tensorflow-cpu instead of standard tensorflow.

# 1. Clone the repository
git clone https://github.com/Piyush-Sharma788/PixelTruth.git
cd PixelTruth

# 2. Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

Model setup

PixelTruth also needs a trained model file before it can make predictions.

Option 1: automatic download on first run

Set a direct download URL for the trained model (for example, a GitHub Release asset) and the app will fetch it automatically when it starts:

export PIXELTRUTH_MODEL_URL=https://your-release-link/deepfake_detection_model.h5
export PIXELTRUTH_MODEL_SHA256=<optional-sha256>
streamlit run app.py

Option 2: manual download script

Download the model with the helper script:

python scripts/download_model.py --url https://your-release-link/deepfake_detection_model.h5 --dest deepfake_detection_model.h5

Option 3: place the file manually

1. Download or generate the trained Keras model.
2. Save it as deepfake_detection_model.h5 in the project root, or set PIXELTRUTH_MODEL_PATH to the file location.
3. Restart the app after placing the model file.

If you keep the model in a different folder, export the path before launching Streamlit:

export PIXELTRUTH_MODEL_PATH=/full/path/to/deepfake_detection_model.h5
streamlit run app.py

If you receive the model from a release asset or shared download link, copy it into the project root first:

cp /path/to/downloaded/deepfake_detection_model.h5 ./deepfake_detection_model.h5

If you publish the model as a GitHub Release asset, the recommended setup is:

1. Upload deepfake_detection_model.h5 to the release.
2. Copy the release asset URL into PIXELTRUTH_MODEL_URL.
3. Optionally record the SHA256 checksum in PIXELTRUTH_MODEL_SHA256.
4. Start the app with streamlit run app.py.

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🚀 Usage

# Run the Streamlit dashboard
streamlit run app.py

Then open your browser at http://localhost:8501, upload an image, and get instant results.

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📁 Project Structure

PixelTruth/
├── app.py              # Streamlit dashboard
├── predict.py          # Inference logic
├── train.py            # Model training (v1)
├── train_v2.py         # Model training (v2)
├── train_v3.py         # Model training (v3)
├── requirements.txt    # Dependencies
├── Figure_1.png        # Training result plot
├── Figure_2.png        # Evaluation plot
└── README.md

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🤝 Contributing

We welcome contributions of all kinds! Whether you're fixing a bug, improving the UI, or adding new features — you're welcome here.

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add your feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

Please read CONTRIBUTING.md for detailed guidelines.

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🌸 GSSoC 2026

PixelTruth is participating in GirlScript Summer of Code 2026!

We have beginner-friendly issues ready. Look for issues labelled:

• good-first-issue
• beginner-friendly
• documentation
• enhancement

Feel free to explore open issues and start contributing!

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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Built with ❤️ for media integrity by Piyush Sharma
If you found this useful, please ⭐ star the repo!

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