Digital watermarking algorithms attract widespread attention due to their important application value in the fields of copyright protection, content authentication, and data hiding. In practical applications, images with embedded watermarks are often affected by differentiable noises such as image distortion and sharpening blurring. At the same time, they also face interference from non-differentiable noises such as JPEG compression and transmission errors. Existing studies mostly focus on scheme design in a single noise environment, or attempt to use differentiable models to approximately simulate non-differentiable noises. These methods limit the robustness of watermarking algorithms to a certain extent. To solve this problem, this study proposes an end-to-end one-stage digital watermarking scheme based on an invertible neural network. The scheme uses an invertible neural network to simulate non-differentiable noise, enhancing the algorithm’s adaptability and robustness to actual noisy environments. Compared with existing algorithms, this algorithm improves the peak signal-to-noise ratio (PSNR) by 3.12 dB and the average extraction accuracy (ACC) by 35.36% in the case of multiple noise superposition.