To address the limitations of existing image splicing and tampering detection methods, such as restricted receptive fields, single-scale feature extraction, and limited extraction capabilities, this paper proposes a multi-scale gated fusion algorithm for image tampering and splicing detection. First, a dual-encoder, single-decoder architecture network is designed: the two encoders utilize standard convolution and dilated convolution to capture features at different scales, while the decoder employs standard convolution. Second, at the shallow layers of the network, the features extracted by the two encoders are added element-wise to fuse dual-path information, which is then passed to the decoder via skip connections to enhance feature representation capabilities. Finally, at the end of the encoder, a multi-scale adaptive gated fusion module is employed to adaptively fuse the local and global features captured by the dual encoders, thereby reducing redundant information and highlighting important features. Experimental results show that the proposed method achieves F1 score improvements of 9.62%, 3.29%, 4.75% and 2.5% on the three public datasets CASIA1.0, CASIA2.0, IMD2020 and a self-created synthetic dataset, respectively; in comparative experiments, the proposed method outperforms other methods in overall detection results; in robustness experiments, the proposed method demonstrates high accuracy and robustness in handling complex scenes and diverse data, effectively detecting tampered regions with performance superior to other methods. The above results indicate that the proposed method provides a robust technical foundation and new research directions for studies and applications in the field of image security.