<p>With advancements in image processing and the proliferation of editing software, image splicing forgery has become increasingly facile to execute yet harder to detect, thereby impacting societal security. Effective detection and localization methods are urgently needed. Existing methods, while somewhat effective, often over-rely on semantic features, overlook shallow features, and struggle to adapt to varying tampered region sizes. To address these issues, we propose a two-stream image splicing forgery localization network, named DMU-Net. The network first introduces a noise stream as a supplementary feature alongside the RGB stream to provide a richer feature representation. Subsequently, we improve the Atrous Spatial Pyramid Pooling module by incorporating an attention mechanism that enables the model to obtain feature maps of different scales, effectively use context information, and enhance the ability to capture tampered regions of various sizes. Finally, we employ a dual attention mechanism to fuse features from both the encoder and decoder stages; this approach effectively leverages shallow features for fusing coarse-grained and fine-grained features, thus enhancing the model’s ability to capture features across different dimensions. Extensive experimental results show that the proposed method outperforms the state-of-the-art image splicing localization methods in terms of detection accuracy and robustness.</p>