Exploring Face De-Identification using Latent Spaces

We explore a new method to hide identity information in a facial image from face recognition (FR) systems, while only minimally changing the appearance of the image as perceived by humans. We train a decoder network that reverses the mapping of an FR system and use the dissimilarity score function of this FR system to teach the decoder to return images with as little identity information as possible, while using a visual loss to change the image as little as possible visually. We show that these obfuscation attacks are also successful when the FR system is unknown. We analyse the obfuscated images in latent space and show that our approach as well as an existing method can be easily circumvented by applying the same obfuscation method to the enrolled faces as to the probe images. We suggest an adaptation that can help prevent this circumvention.