An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks
An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks
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DOI：
10.1109/tmm.2020.3006415
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发表时间：
2021-01-01
影响因子：
7.3
通讯作者：
Shih, Frank Y.
中科院分区：
文献类型：
--
作者：
Zhong, Xin;Huang, Pei-Chi;Shih, Frank Y.
数字图像水印是在封面图像上隐蔽地嵌入和提取水印的过程。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近年来受到越来越多的关注。然而，现有的基于深度学习的水印方法既没有充分利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性。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。为了最大限度地减少对领域知识的要求，利用深度神经网络的拟合能力来学习和推广一种自动图像水印算法。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。为了方便灵活的应用，所提出方案的鲁棒性不需要任何先验知识或可能的攻击的对抗示例。一个具有挑战性的从手机摄像头捕获的图像中提取水印的案例证明了该方案的鲁棒性和实用性。实验、评价和应用实例验证了该方案的优越性。
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.