Advances in photo editing and manipulation tools have made it significantly easier to create fake imagery. Learning to detect such manipulations, however, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. We propose to address this problem by developing new, sample-efficient learning methods that can learn to detect fake images with minimal labeled training data.
