Multimedia security in screen-shooting channel transmission remains a major challenge in the digital watermarking research. In view of the problems of differences in light intensities and sampling distortions during the screen-shooting process, watermarking schemes designed based on digital channels are not suitable for screen-shooting channels. Therefore, a screen-shooting resilient watermarking scheme based on invertible neural networks in the wavelet domain was proposed to address the issue of "cross-media robustness". First, the watermark message and the original image were preprocessed using a preprocessing network based on the U-Net structure to generate the residual image. Next, the residual image was subjected to discrete wavelet transform with the original image, and the invertible neural network was used to embed and extract watermarks in the wavelet domain. Finally, a noise pool was integrated into the model training process to enhance the robustness against screen-shooting noise attacks. Simulation results demonstrate that the proposed scheme generates watermarked images with better visual quality and achieves high accuracy of watermark extraction at different distances, angles, and light intensities of screen-shooting.