Deepfake Image Generation using AI Models

This project demonstrates the capabilities of deep learning models to generate deepfake images. By utilizing state-of-the-art Generative Adversarial Networks (GANs), this project creates highly realistic altered images that are difficult to distinguish from authentic ones.

Features

• Generates realistic deepfake images using advanced GAN architectures.
• Ability to swap faces, alter expressions, or synthesize completely new faces.
• Customizable parameters for tuning the level of realism and style.
• Includes tools for training custom models with new datasets.

Installation

To set up the Deepfake Image Generation system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/deepfake-generator.git
   cd deepfake-generator

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To generate a deepfake image, run the main script and provide the necessary input images and parameters:

python generate_deepfake.py --source "source_image.jpg" --target "target_image.jpg"

Adjust the script parameters to control the quality and style of the generated deepfake images.

Results

The generated images illustrate the model’s ability to create highly realistic alterations that are visually convincing and often indistinguishable from real photos.