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.

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 GR that translates manipulated image to the real image domain, a generative annotation model GA that estimates the pixel-wise probability of image patch being either real or fake, and two discriminators DR and DA that qualify the output of GR and GA. The aim of model GR is to maximize the probability of model GA making a mistake.

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_R that translates manipulated image to the real image domain, a generative annotation model G_A that estimates the pixel-wise probability of image patch being either real or fake, and two discriminators D_R and D_A that qualify the output of G_R and G_A. The aim of model G_R is to maximize the probability of model G_A making a mistake.