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