Maliciously tampered images pose serious threats to both daily life and society. Although numerous detection models have been developed for image tampering detection, they still suffer from issues such as the loss of fine details and insufficient capability in detecting small targets. To address these challenges, this study proposes a multi-scale cross-layer fusion-based for image tampering localization. Built upon the RRU-Net framework, the model changes the skip connection scheme and enhances the encoder features in skip connections using the MSC-SC structure with atrous spatial pyramid pooling. A cross-layer fusion module is then designed to adaptively fuse the encoder and decoder feature maps. In addition, a triple attention mechanism is introduced to enhance feature perception during the down sampling process. Finally, a joint loss function is utilized to alleviate the imbalance between positive and negative samples. Experiments conducted on the CASIA v2 and COLUMB datasets demonstrate that the proposed method achieves F1 score improvements of 11.33% and 6.84%, respectively, compared with the original RRU-Net, indicating that significant effectiveness is achieved.