README.md

metadata

title: ImageProof – Deep learning AI-generated Image Detection
emoji: 🚗
colorFrom: yellow
colorTo: blue
sdk: streamlit
sdk_version: 1.40.0
app_file: app.py
pinned: false
license: mit

ImageProof - AI Image Authenticity Detector 🧠

A Streamlit-based web application that uses a fine-tuned EfficientNet-B3 model to detect whether images are AI-generated or real.

Table of Contents

• Demo
• Features
• Installation
• Usage
• Contributing
• License

Demo

Check out the application in action with these demo files:

Features

• Image Upload: Support for JPG, JPEG, and PNG files.
• URL Input: Analyze images directly from web URLs.
• Real-time Prediction: Instant classification with confidence scores.
• Interactive UI: Built with Streamlit for easy use.
• Model Integration: Leverages EfficientNet-B3 for accurate detection.

Installation

To get started, clone the repository and set up a virtual environment.

# Create a virtual environment
python -m venv .venv

# Activate it
# On Linux/Mac:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Usage

Run the application using Streamlit:

streamlit run app.py

1. Open the app in your browser.
2. Choose to upload an image or enter an image URL.
3. View the prediction results, including the label (AI-generated or Real) and confidence score.

Example prediction output:

• Label: 🧠 AI-generated
• Confidence: 0.95

Contributing

Contributions are welcome! Please fork the repository and submit a pull request. Ensure code follows best practices and includes tests.

License

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

Acknowledgements

• Built with Streamlit for the web interface.
• Model based on EfficientNet and timm.
• Thanks to the open-source community for PyTorch and related libraries.