When deep learning meets watermarking: A survey of application, attacks and defenses

Abstract

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.