Invisible and robust watermarking model based on hierarchical residual fusion multi-scale convolution

In current deep learning based watermarking technologies, it remains challenging to fully integrate the features of watermark and cover image. Most watermarking models with fixed-size kernel convolution exhibit restricted feature extraction ability, leading to incomplete feature fusion. To address this issue, a hierarchical residual fusion multi-scale convolution (HRFMS) module is designed. The method extracts image features from various receptive fields and implements feature interaction by residual connection. To produce watermarked image with high visual quality and attack resistance, a watermarking model based on the HRFMS is devised to achieve multi-scale feature fusion. Moreover, to minimize image distortion caused by watermark information, an attention mask layer is designed to guide the distribution of watermark information. The experimental results demonstrate that the invisibility and the robustness of the HRFMSNet are excellent. The watermarked images generated by the HRFMSNet are nearly visually indistinguishable from the cover images. The average peak signal-to-noise ratio of the watermarked images is 37.13 dB, and most of the bit error rates of the decoded messages are below 0.02.