Generative Adversarial Network (GAN) for Generating Fake Signatures

Model Architecture

The code implements a GAN with the following components:

• Generator: A fully connected neural network that takes a noise vector as input and generates a synthetic signature image. It consists of multiple layers with batch normalization and ReLU activations, with a final layer using a sigmoid activation function to output an image.
• Discriminator: A fully connected neural network that takes an image as input (either real or generated) and outputs a probability indicating whether the image is real or fake. It uses Leaky ReLU activations to improve learning stability.

Data Preprocessing

• The dataset consists of real signatures stored in a directory.
• Images are resized to 64x64 and normalized using torchvision transforms.
• A PyTorch DataLoader is used to load the dataset in batches.

Training Process

1. Forward Pass: The generator creates fake images from random noise, and the discriminator evaluates both real and fake images.
2. Discriminator Loss: Compares real and fake images, penalizing incorrect classifications using Binary Cross-Entropy (BCE) loss.
3. Generator Loss: Encourages the generator to create more realistic images by maximizing the discriminator's misclassification of fake images.
4. Backpropagation: Both networks update their weights using Adam optimization.
5. Image Visualization: Periodically, generated images are displayed to monitor training progress.
6. Model Saving: The trained generator is saved as generator_model.pth for future image generation.

Signature Generation

• The trained generator is loaded and used to generate synthetic signatures by inputting random noise vectors.
• Generated images are visualized and can be saved for further analysis.

Code Implementation

• gan-signature-generation.ipynb: Contains the entire implementation, including data loading, model training, evaluation, and image generation.
• Key Functions & Classes:
  • Generator: Defines the neural network for generating images.
  • Discriminator: Defines the neural network for distinguishing real and fake images.
  • get_noise(): Generates random noise for the generator.
  • disc_loss() & gen_loss(): Compute losses for training.
  • train_loop: Runs the training process.
  • show_tensor_images(): Displays generated images.

Output

• Generated signature images are displayed during training and can be found in the output directory.
• The trained generator can be used for further signature synthesis applications.