A Discriminative Multi-Channel Noise Feature Representation Model for Image Manipulation Localization

Abstract

Noise feature modules are commonly used in image manipulation localization. However, different noise learning modules can only target limited tampering methods. In actual image tampering localization tasks, the tampering methods are unknown; it is difficult for a single noise feature module to match all tampering methods. In this paper, we explore the ability of different noise feature modules to localize different manipulation types. Furthermore, we propose a Multi-Channel Noise Feature (MCNF) representation model to describe the noise feature of the tampered images. MCNF contains multiple noise feature modules, which can automatically discriminate the noise feature modules that are practical for localization results. Experiments on several standard image tampering datasets show that our MCNF model achieves state-of-the-art performance compared to alternative methods.