Face Anonymization Pipeline in Pytorch

Protecting data privacy is critical to preserving customer trust and is also gaining increasing attention from policy makers. Staying ahead of these expectations requires continual improvements to AI toolchains. Anonymizing image data is particularly challenging without badly degrading the quality of the image samples. We developed the capability to anonymize images while preserving the image distribution, giving us an excellent way to maintain the anonymity of the persons in the images while still performing data augmentation tasks. Our approach is based on the paper, "DeepPrivacy: A Generative Adversarial Network for Face Anonymization," published in 2019 at the International Symposium on Visual Computing.