The technique of digital watermarking has emerged as a crucial method for safeguarding the intellectual property rights of image owners. Substantial advancements have been made in the field of embedding watermarks in digital images. However, meeting the requirements of high-capacity, imperceptibility, and robustness simultaneously poses a challenge due to the trade-off between these factors. We propose a model that establishes a mutual mapping between the cover image and watermarked image domains, which enables alignment and can accelerate the model convergence. Our model prioritizes the decoder structure by utilizing an encoder-like architecture to align the overall structure of the model. This design improves the decoder’s capacity to extract watermark information. In the process of image generation, we present an approach to enhance feature size through antispectral aliasing, which replaces the transposed convolution mechanism in the U-Net structure. This technique significantly reduces high-frequency artifacts, leading to an overall improvement in the quality of the watermarked image. To improve the ability of the model to deceive the discriminator, we introduce a discriminator that imposes constraints on multiple frequency bands, resulting in significantly improved imperceptibility. Our model demonstrates superior robustness compared to state-of-the-art methods while preserving a competitive level of image quality.