Learning to Conceal: A Method for Preserving Privacy and Avoiding Prejudice in Images

We introduce a learning model able to conceal personal information (e.g. gender, age, ethnicity, etc.) from an image while maintaining any additional information present in the image (e.g. smile, hair-style, brightness). Our trained model is not provided the information that it is concealing, and does not try learning it either. Namely, we created a variational autoencoder (VAE) model that is trained on a dataset including labels of the information one would like to conceal (e.g. gender, ethnicity, age). These labels are directly added to the VAE's sampled latent vector. Due to the limited number of neurons in the latent vector and its appended noise, the VAE avoids learning any relation between the given images and the given labels, as those are given directly. Therefore, the encoded image lacks any of the information one wishes to conceal. The encoding may be decoded back into an image according to any provided properties (e.g. a 40-year old woman). Our method successfully conceals the private information; a convolutional neural network trained on the concealed images cannot restore the original private information. In contrast to the private information, a user study shows that the remaining properties of the original image carry-on to the concealed image. The proposed architecture can be used as a mean for privacy preserving and can serve as an input to systems, which will become unbiased and not suffer from prejudice.