To address the problem that feature extraction by increasing the depth in image splicing forgery detection algorithm based on convolutional neural network (CNN) can easily lead to loss of shallow forgery trace features, which causes a decrease in image resolution, this paper proposes an improved ringed residual U-net (RRU-Net) dual-view multiscale image splicing forgery detection algorithm. First, the noise image is generated by multifield fusion, and the noise perspective is generated through the high-pass filter of the spatial rich model (SRM), to enhance edge information learning. Second, the multiscale feature extraction module is designed by combining the original view with continuous downsampling of the noisy view to obtain the multiscale semantic information of the image. Finally, the A²-Nets dual-attention network is introduced to effectively capture the global information and accurately locate the tampered area of ??the image. Compared with the original RRU-Net, the algorithm in this study shows a significant detection effect and robustness improvement on multiple data sets, demonstrating significant progress in the field of image forgery detection. These results show that the proposed method has higher accuracy and reliability when dealing with complex scenes and diversified data, providing important technical support for research and application in the field of image security and information protection.