Traditional watermarking algorithms have been extensively studied. As an important type of watermarking schemes, template-based approaches maintain a very high embedding rate. In such scheme, the message is often represented by some dedicatedly designed templates, and then the message embedding process is carried out by additive operation with the templates and the host image. To resist potential distortions, these templates often need to contain some special statistical features so that they can be successfully recovered at the extracting side. But in existing methods, most of these features are handcrafted and too simple, thus making them not robust enough to resist serious distortions unless very strong and obvious templates are used. Inspired by the powerful feature learning capacity of deep neural network, we propose the first deep template-based watermarking algorithm in this paper. Specifically, at the embedding side, we first design two new templates for message embedding and locating, which is achieved by leveraging the special properties of human visual system, i.e. , insensitivity to specific chrominance components, the proximity principle and the oblique effect. At the extracting side, we propose a novel two-stage deep neural network, which consists of an auxiliary enhancing sub-network and a classification sub-network. Thanks to the power of deep neural networks, our method achieves both digital editing resilience and camera shooting resilience based on typical application scenarios. Through extensive experiments, we demonstrate that the proposed method can achieve much better robustness than existing methods while guaranteeing the original visual quality.

中文翻译：

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基于深度模板的水印

传统的水印算法已经被广泛研究。作为一种重要的水印方案，基于模板的方法可以保持很高的嵌入率。在这种方案中，消息通常由一些专门设计的模板表示，然后通过对模板和主机映像进行加法运算来执行消息嵌入过程。为了抵抗潜在的失真，这些模板通常需要包含一些特殊的统计功能，以便可以在提取端成功恢复它们。但是在现有方法中，大多数这些功能都是手工制作的并且过于简单，因此，除非使用非常强大且明显的模板，否则它们将不足以抵抗严重的失真。受到深度神经网络强大的功能学习能力的启发，我们在本文中提出了第一个基于深度模板的水印算法。具体来说，在嵌入方面，我们首先设计了两个用于消息嵌入和定位的新模板，这是通过利用人类视觉系统的特殊属性来实现的，IE ，对特定色度分量不敏感，接近原理和倾斜效果。在提取方面，我们提出了一种新颖的两级深度神经网络，该网络由辅助增强子网络和分类子网络组成。借助深度神经网络的强大功能，我们的方法可以基于典型的应用场景同时实现数字编辑弹性和相机拍摄弹性。通过广泛的实验，我们证明了该方法可以在保证原始视觉质量的同时，比现有方法具有更好的鲁棒性。