Generative Adversarial Networks (GANs) have emerged as a powerful tool for image augmentation, offering a more sophisticated approach to expanding training datasets. Unlike traditional methods that rely on simple transformations like rotations and flips, GAN-based augmentation uses the generative capabilities of these networks to synthesize highly realistic and diverse variations of existing images. This chapter will explore the principles and applications of GAN-based image augmentation, highlighting its potential to significantly improve the performance and robustness of deep learning models in computer vision.