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Digital image watermarking is the process of embedding and extracting awatermark covertly on a cover-image. To dynamically adapt image watermarkingalgorithms, deep learning-based image watermarking schemes have attractedincreased attention during recent years. However, existing deep learning-basedwatermarking methods neither fully apply the fitting ability to learn andautomate the embedding and extracting algorithms, nor achieve the properties ofrobustness and blindness simultaneously. In this paper, a robust and blindimage watermarking scheme based on deep learning neural networks is proposed.To minimize the requirement of domain knowledge, the fitting ability of deepneural networks is exploited to learn and generalize an automated imagewatermarking algorithm. A deep learning architecture is specially designed forimage watermarking tasks, which will be trained in an unsupervised manner toavoid human intervention and annotation. To facilitate flexible applications,the robustness of the proposed scheme is achieved without requiring any priorknowledge or adversarial examples of possible attacks. A challenging case ofwatermark extraction from phone camera-captured images demonstrates therobustness and practicality of the proposal. The experiments, evaluation, andapplication cases confirm the superiority of the proposed scheme.

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks