Pixel privacy: Increasing image appeal while blocking automatic inference of sensitive scene information

We introduce a new privacy task focused on images that users share online. The task benchmarks image transformation algorithms that are capable of blocking the ability of automatic classifiers to infer sensitive information in images. At the same time, the image transformations should maintain the original value of the image to the user who is sharing it, either by leaving it not obviously changed, or by enhancing it to increase its visual appeal. This year, the focus is on a set of 60 scene categories, selected from the Places365-Standard dataset, that can be considered privacy-sensitive. Copyright held by the owner/author(s).