Learning to localize image forgery using end-to-end attention network

Abstract

Recent advancements have increased the prevalence of digital image tampering. Anyone can manipulate multimedia content using editing software to alter the semantic meaning of images to deceive viewers. Since manipulations appear realistic, both humans and machines face challenges detecting forgeries. We propose a novel algorithm for authenticating visual content by localizing forged regions in this work. Our proposed algorithm employs channel attention convolutional blocks in an end-to-end learning framework. The channel attention infers forged regions in an image by extracting attention-aware multi-resolution features in the spatial domain and features in the frequency domain. Therefore, the proposed network is divided into two subnetworks, for extracting attention-aware multi-resolution features in the spatial and frequency domain. To predict the resulting mask, we concatenate the features of both networks. The proposed channel attention network exclusively focuses on the forged region and increases network generalization capabilities on unseen manipulations. Rigorous experiments demonstrate that the proposed algorithm outperforms state-of-the-art methods on five benchmark datasets for localizing a wide range of manipulations.