An image splicing forgery detection algorithm based on a dual-label supervised dual-stream ringed residual u-net(RRU-Net) was proposed to address existing issues such as blurred edges and neglected background information in current methods. Firstly, building upon the RRU-Net, a mask edge stream was designed. Bidirectional interaction between the mask stream and the mask edge stream was achieved through an adaptive layer, which fully utilized their complementary information to enhance the clarity of the detected mask edges. Secondly, at the input stage, the input image was converted from the RGB color space to the YCrCb color space, allowing the luminance and chrominance information to be analyzed separately to reveal potential tampering traces. Subsequently, a coordinate attention mechanism was incorporated into the intermediate layers of the network to recalibrate the fused features, thereby enhancing the focus on critical features. Finally, within the mask stream, a combination of binary cross entropy loss(BCE) and Dice loss functions was employed to mitigate the negative impact of foreground-background (area) imbalance in the samples. The experimental results demonstrate that compared to the baseline model RRU-Net, the F₁-score of the proposed method is increased by 25.96%, 5.15%, 7.14%, and 9.96% on the public datasets CASIA1.0, CASIA2.0, IMD2020, and the self-constructed synthetic dataset, respectively. Moreover, the comprehensive detection performance of the proposed method is found to be significantly superior to all other compared methods in the study. Furthermore, in robustness experiments, the proposed method is subjected to post-processing operations such as JPEG compression and image scaling. Although a slight decrease in F₁-score is observed, the method generally outperforms the comparative approaches, demonstrating strong robustness. The superior detection performance of the method is further validated.