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.

DeepPrivacy is a fully automatic anonymization technique for images. This repository contains the source code for the paper "DeepPrivacy: A Generative Adversarial Network for Face Anonymization", published at ISVC 2019. The DeepPrivacy GAN never sees any privacy sensitive information, ensuring a fully anonymized image. It utilizes bounding box annotation to identify the privacy-sensitive area, and sparse pose information to guide the network in difficult scenarios. DeepPrivacy detects faces with state-of-the-art detection methods.

Was George Orwell right, is Big Brother watching us? Undoubtedly many are alarmed by the ever-increasing level of computer-driven surveillance, particularly involving facial recognition technologies. In the past few months, San Francisco and Oakland, California, and the US state of Massachusetts have all banned police from using facial recognition tech. Meanwhile, in Europe, The General Data Protection Regulation (GDPR) introduces restrictive rules about privacy preservation in data processing. A team of researchers from the Norwegian University of Science and Technology recently proposed a new architecture that can anonymize faces in images automatically while the original data distribution remains uninterrupted.

We propose a novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution. We ensure total anonymization of all faces in an image by generating images exclusively on privacy-safe information. Our model is based on a conditional generative adversarial network, generating images considering the original pose and image background. The conditional information enables us to generate highly realistic faces with a seamless transition between the generated face and the existing background. Furthermore, we introduce a diverse dataset of human faces, including unconventional poses, occluded faces, and a vast variability in backgrounds. Finally, we present experimental results reflecting the capability of our model to anonymize images while preserving the data distribution, making the data suitable for further training of deep learning models. As far as we know, no other solution has been proposed that guarantees the anonymization of faces while generating realistic images.