Dual‐Stream Fusion and Multi‐scale Analysis: Introducing the Synergistic Dual‐Stream Network (SDS‐Net) for Image Manipulation Segmentation

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. Introducing synergistic dual‐stream network (SDS‐Net), a groundbreaking deep learning framework for image manipulation detection. This innovative system employs a dual‐stream fusion strategy, enhancing feature extraction capabilities and ensures precise localization of tampered regions. SDS‐Net's unique approach significantly outperforms existing methods, offering robust and accurate detection across different datasets.