ToonerGAN: Reinforcing GANs for Obfuscating Automated Facial Indexing

The rapid evolution of automatic facial indexing technologies increases the risk of compromising personal and sensitive information. To mitigate the issue, we propose creating cartoon avatars, or 'toon avatars', designed to effectively obscure identity features. The primary objective is to deceive current AI systems, preventing them from accurately identifying individuals while making minimal modifications to their facial features. Moreover, we aim to ensure that a human observer can still recognize the person depicted in these altered avatar images. To achieve this, we introduce 'ToonerGAN', a novel approach that utilizes Generative Adversarial Networks (GANs) to craft personalized cartoon avatars. The ToonerGAN framework consists of a style module and a de-identification module that work together to produce high-resolution, realistic cartoon images. For the efficient training of our network, we have developed ‘ToonSet' dataset, consisting of around 23,000 facial images and their cartoon renditions. Through comprehensive experiments and benchmarking against existing datasets, including CelebA-HQ, our method demonstrates superior performance in obfuscating identity while preserving the utility of data. Additionally, a user-centric study exploring the effectiveness of ToonerGAN has yielded compelling observations.