Deep learning has been used to address various problems in a range of domains within both academia and industry. However, the issue of intellectual property with deep learning models has aroused broad attention. Watermarking, a proactive defense approach widely adopted to safeguard the copyright of digital content, is now sparking novel mechanisms for protecting the intellectual property of deep learning models. Further, significantly improved digital watermarking techniques have been developed to protect multimedia content, primarily images, with high efficiency and effectiveness. Yet, our current understandings of these two technical forefronts, i.e., deep learning model watermarking and image watermarking via deep learning, are unilaterally separated and application-oriented. To this end, we have undertaken a survey on emerging watermarking mechanisms in the two areas from a novel security perspective. That is, we have surveyed attacks and defenses in deep learning model watermarking and deep-learning-based image watermarking. Within the survey, we propose an objective taxonomy to unify the two domains, revealing their commonly shared properties with reference to design principles, functionalities, etc. Upon the taxonomy, a comprehensive analysis of attacks and defenses associated with the shared properties in both domains is presented. We have summarized the collected methods from a technical aspect and their advantages vs. disadvantages. A discussion of the joint characteristics and possible improvements of the methods are attached. Lastly, we have also proposed several potential research directions to inspire more ideas in these areas.

深度学习已被用来解决学术界和工业界一系列领域的各种问题。然而，深度学习模型的知识产权问题引起了广泛关注。水印是一种广泛用于保护数字内容版权的主动防御方法，目前正在引发保护深度学习模型知识产权的新机制。此外，已经开发出显着改进的数字水印技术来以高效率和有效性保护多媒体内容，主要是图像。然而，目前我们对深度学习模型水印和基于深度学习的图像水印这两个技术前沿的认识是片面割裂的、面向应用的。为此，我们从新颖的安全角度对这两个领域的新兴水印机制进行了调查。也就是说，我们调查了深度学习模型水印和基于深度学习的图像水印的攻击和防御。在调查中，我们提出了一个客观的分类法来统一这两个领域，参考设计原则、功能等揭示它们共同的属性。根据分类法，对与两个领域的共享属性相关的攻击和防御进行全面分析呈现。我们从技术角度总结了收集到的方法及其优缺点。附件中还讨论了联合特性和可能的​​方法改进。最后，我们还提出了几个潜在的研究方向，以激发这些领域的更多想法。