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.

中文翻译：

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一种基于深度神经网络的自动化鲁棒图像水印方案

数字图像水印是在覆盖图像上隐蔽地嵌入和提取水印的过程。近年来，为了动态适应图像水印算法，基于深度学习的图像水印方案引起了越来越多的关注。然而，现有的基于深度学习的水印方法并没有充分应用拟合能力来学习和自动化嵌入和提取算法，也没有同时实现鲁棒性和盲目性的特性。本文提出了一种基于深度学习神经网络的鲁棒盲图像水印方案。为了最小化领域知识的要求，利用深度神经网络的拟合能力来学习和推广自动图像水印算法。深度学习架构是专门为图像水印任务设计的，它将以无监督的方式进行训练，以避免人为干预和注释。为了促进灵活的应用，所提出的方案的鲁棒性是在不需要任何先验知识或可能攻击的对抗性示例的情况下实现的。从手机摄像头捕获的图像中提取水印的一个具有挑战性的案例证明了该提案的鲁棒性和实用性。实验、评估和应用案例证实了所提出方案的优越性。从手机摄像头捕获的图像中提取水印的一个具有挑战性的案例证明了该提案的鲁棒性和实用性。实验、评估和应用案例证实了所提出方案的优越性。从手机摄像头捕获的图像中提取水印的一个具有挑战性的案例证明了该提案的鲁棒性和实用性。实验、评估和应用案例证实了所提出方案的优越性。