A convolutional neural network-based blind robust image watermarking approach exploiting the frequency domain

Image watermarking embeds information in the image that is visually imperceptible and can be recovered even if the image is modified or attacked during distribution, thus protecting the image copyright. Current image watermarking methods make the learned model resistant to attacks by simulating specific attacks but lack robustness to unspecified attacks. In this paper, we propose to hide the information in the frequency domain. To control the distribution and intensity of watermarking information, we introduce a channel weighting module based on modified Gaussian distribution. In the spatial domain, we design a spatial weighting module to improve the watermarking visual quality. Moreover, a channel attention enhancement module designed in the frequency domain senses the distribution of watermarking information and enhances the frequency domain channel signals to improve the watermarking robustness. Abundant experimental results show that our method guarantees high image visual quality and high watermarking capacity. The generated watermarking images can robustly resist unspecified attacks such as noise, crop, blur, color transform, JPEG compression, and screen-shooting.