We propose a novel Tri-Path Backbone Network (TPB-Net) and train it end-to-end to effectively detect multiple types of image manipulations. The key challenge for image manipulation localization lies in the difficulty and diversity of extracting forgery features. To address this, we adopt a Triple-path Interconnected Backbone (TIB) scheme as the feature extractor, which enables the strong feature detection capabilities. Furthermore, we design and introduce the Dual-path Compressed Sensing Attention (DCSA) module, that incorporates a dual-path attention mechanism. The DCSA module intelligently compresses channels in the spatial path and spatial information in the channel path. These compression operations lead to improved learning efficiency, enhanced representation effectiveness, and increased model robustness. TPB-Net offers an end-to-end framework comprising trainable modules, facilitating joint optimization and enabling the achievement of optimal performance. Through rigorous experiments conducted on four standard image manipulation datasets, we demonstrate the superior performance of our method compared to previous state-of-the-art approaches.

This article introduces the synergistic dual‐stream network (SDS‐Net), a novel neural network architecture that significantly enhances the detection of image manipulations. SDS‐Net employs a unique dual‐stream fusion strategy that processes both RGB image and the corresponding noise map. It innovatively combines features computation from the different blocks of dual backbones, and leverages a multi‐scale spatial pyramid pooling (MSPP) module to expand the receptive fields of shallow features. This approach not only enriches the feature representation, but also ensures the precise localization of manipulated regions. Extensive experiments conducted on various public datasets demonstrate the superiority of SDS‐Net over several state‐of‐the‐art methods.