DS-UNet: A dual streams UNet for refined image forgery localization

Abstract

Nowadays, image manipulation has become very easy with the help of image editing applications. The abuse of tampered images would mislead people’s judgment, or even make the society suffer from instability if without any interference. To address this issue, a dual-stream UNet named DS-UNet is presented in this paper to detect image tampering and localize forgery areas. The DS-UNet employs an RGB stream to extract both high-level and low-level manipulated traces for a coarse localization, and adopts a Noise stream to expose the local noise inconsistency for a refined localization. The lightweight hierarchical fusion way provides the DS-UNet with the ability to perceive tampered objects at different scales for the reason that tampered objects always vary in shapes and sizes. After that, the DS-UNet applies a single decoder to receive the abundant low-level manipulated traces and spatial localization information in the skip connection path. Through the decoder, the object details and spatial dimensions are gradually recovered, and the high-resolution prediction maps are generated. In the comparative analysis, more evaluation indices are introduced than the existing works in order to obtain a more comprehensive assessment. Extensive experiments with satisfying results on five datasets demonstrate its superior performance over state-of-the-art methods.