Anonymizing Personal Images Using Generative Adversarial Networks

Abstract

This paper introduces a first approach on using Generative Adversarial Networks (GANs) for the generation of fake images, with the objective of anonymizing patients information in the health sector. This is intended to create valuable images that can be used both, in educational and research areas, while avoiding the risk of a sensitive data leakage. For this purpose, firstly a thorough research on GAN’s state of the art and available databases has been developed. The outcome of the research is a GAN system prototype adapted to generate personal images that imitates provided samples. The performance of this prototype has been checked and satisfactory results have been obtained. Moreover, a novel research pathway has been opened so further research can be developed.