The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection

Modern photo editing tools allow creating realistic manipulated images easily. While fake images can be quickly generated, learning models for their detection is challenging due to the high variety of tampering artifacts and the lack of large labeled datasets of manipulated images. In this paper, we propose a new framework for training of discriminative segmentation model via an adversarial process. We simultaneously train four models: a generative retouching model G A that estimates the pixel-wise probability of image patch being either real or fake, and two discriminators D A that qualify the output of G A. The aim of model G A making a mistake. Our method extends the generative adversarial networks framework with two main contributions: (1) training of a generative model G A that learns rich scene semantics for manipulated region detection, (2) proposing per class semantic loss that facilitates semantically consistent image retouching by the G_R.