AI Generated Image Detection

AI Detection Model: Differentiating Synthetic and Real Images

Overview

This project involves the development of an advanced AI detection model designed to distinguish between synthetic and real images. By leveraging Generative Adversarial Networks (GAN) and Region-based Convolutional Neural Networks (RCNN), we have created a robust and efficient system capable of high-accuracy image classification.

Key Features

• Engineered AI Detection Model: Utilizes state-of-the-art GAN and RCNN architectures to effectively differentiate between synthetic and real images.
• Optimized Data Processing: Achieved a significant reduction in dataset processing time, improving from 2 hours to just 10 milliseconds. This optimization greatly enhances the operational efficiency and responsiveness of the model.

Technical Details

1. Model Architecture:

   • Generative Adversarial Networks (GAN): Employed to generate high-quality synthetic images, serving as the basis for training the detection model.
   • Region-based Convolutional Neural Network (RCNN): Used for accurate image detection and classification, ensuring precise differentiation between synthetic and real images.

2. Dataset Processing Optimization:

   • Implemented advanced data processing techniques to reduce the overall processing time.
   • Utilized efficient data handling and preprocessing methods to streamline the workflow, resulting in a drastic reduction in processing time from 2 hours to 10 milliseconds.

Installation and Usage

To get started with this project, follow the steps below:

1. Clone the Repository:

   git clone https://github.com/farihashk/AI-Generated-Image-Detection.git
   cd AI-Generated-Image-Detection

2. Install Dependencies: Ensure you have Python and the necessary libraries installed. You can install the required packages using:

   pip install -r requirements.txt

3. Run the Model: Run the model in Jupyter Notebook or Google Colab

Results

The AI detection model has demonstrated impressive performance in distinguishing between synthetic and real images, with a significant improvement in processing speed and accuracy.

Contributing

We welcome contributions to this project. If you have suggestions, bug reports, or feature requests, please open an issue or submit a pull request.

License

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

Contact

For any questions or inquiries, please contact [email protected].