数字视频在当前通信世界中被认为是一种重要而有效的媒体，广泛应用于新闻、短视频和有线网络广播视频节目中。随着计算机与互联网技术的发展，数字视频内容容易被侵权使用者肆意复制和传播，如何保护视频版权日益成为人们关注的问题。鲁棒视频水印是实现视频版权保护的一种有效手段。作为数字视频水印的分支，鲁棒视频水印是一种通过特定算法在需要被保护的视频对象中嵌入秘密信息——水印来证明版权归属的技术。本文对当前的视频水印技术进行了概述，对视频水印的概念、应用场景、分类方式、设计要求、发展历程和相关经典方法进行了介绍和梳理。本文归纳总结了2016—2021年鲁棒视频水印相关研究工作，包括基于内容的、基于码流的、基于深度学习和其他类型视频水印，并对其中部分工作进行了相应的性能比较和分析。其中，基于内容的视频水印方法将视频看做帧序列，由于在每一帧上应用水印算法，不考虑视频的编解码过程，这类方法实现简单，计算效率高；基于码流的视频水印方法将水印嵌入到编码比特流中，该方案更快速，故可支持实时视频水印应用；基于深度学习的方法取代了依靠手工设计的特征来提高水印的性能。最后分析了鲁棒视频水印的未来发展趋势。

Digital video is an essential effective medium in the communication world nowadays. It is widely used in news, short video and cable network broadcast video programs. Digital video content is easily copied and spread for infringing users, which makes the digital video face severe privacy difficulties. Video copyright has been concerned consistently. In order to protect and claim video ownership, robust video watermarking is one of the important techniques. As a branch of digital video watermarking, robust video watermarking is a technology to authorize copyright ownership. Embedding secret information into the video object needs to be protected by a specific algorithm. This article shows an overview of video watermarking technology. The application scenarios of different types of video watermarking are introduced, including copyright protection, content protection, content authentication, content filtering, broadcast monitoring and online search overall. The classification methods of video watermarking is illustrated, which can be classified based on watermarking attributes and carrier objects. The main properties of watermarking have been embedding capacity, perceptibility, and robustness. In accordance with the existence of embedding capacity, video watermarking can be divided into zero watermarking and non-zero watermarking. Video watermarking can be divided into visible and invisible watermarking in terms of its perceptibility. Video watermarking can be divided into robust video watermarking, semi-fragile video watermarking, and fragile video watermarking via the robustness. Carrier video formats has mainly evolved 2D video, 3D video and virtual reality(VR) video, among which 2D video is the mainstream video format at present. 2D video watermarking can be further classified based on embedding method and extraction method, in which the embedding method can be divided into content-based video watermarking and bit stream-based video watermarking, and the extraction method can be divided into non-blind extraction, blind detection and semi-blind extraction.Moreover, this paper classifies classical video watermarking methods and video watermarking methods that have emerged in the past five years. A content-based and a bitstream-based perspective have been summarized each. Content-based video watermarking has treated video as a collection of images and watermarking application algorithm on each frame. This simplified method is easy to implement costly. Bitstream-based video watermarking embeds copyright information into video in video encoding and decoding process. Since the embedding process of this type of scheme can run in parallel with the video encoding and decoding process, this scheme is faster and more practical than content-based method. As a consequence, it supports real-time video watermarking applications. In the past five years, researchers have proposed video watermarking schemes based on deep learning and video watermarking methods for new video carriers such as 3D and VR. Among them, the video watermarking scheme based on deep learning replaces hand-designed features to improve the performance of video watermarking scheme. The sequential HiDDeN and StegaStamp data sets have been proposed in the intial stage The current deep learning-based video watermarking methods are all content-based watermarking and have not been developed to bitstream-based watermarking yet. In addition, the types of attacks can be resisted via the video watermarking deep learning method at the early stage. According to the two representations of 3D video, 3D video watermarking is divided into watermarking based on stereo imaging and watermarking based on depth image based rendering(DIBR). The designation of DIBR-based 3D video watermarking not only needs to meet the requirements of 2D video watermarking such as robustness and imperceptibility, but also needs to meet the robustness of the DIBR process. Subsequently, this paper makes a classification and comparative analysis of the video watermarking methods that have emerged in the past five years. The performance of video watermarking scheme is evaluated from the two perspectives of video quality and robustness. The video quality evaluation indices include peak signal to noise ratio(PSNR) and structural similarity index(SSIM) and the robustness evaluation indices include bit error rate(BER), mean opinion score(MOS), and false negative rate(FNR). The relationship between performance of video watermarking and different evaluation index value is not same. The larger the PSNR value is, the higher the visual quality of the video produced. The smaller the BER value is, better robustness of the scheme gained. The larger the normalized cross correlation(NCC) value is, the better the robustness of the scheme formed. The capability of each method has been listed to resist various types of attacks more intuitively in the form of a table. Based on comparative analysis, it is concluded that the robustness against temporal synchronization attacks such as frame deleting, frame averaging, frame inserting, and frame rate conversion in the context of embedded frames issue. If the same watermark is embedded in all video frames, it tends to have poor imperceptibility and poor robustness to video frame cutting, frame averaging, and frame exchanging.If different watermarks are embedded in all key frames according to various video scenes, the robustness to video attacks is improved, especially in the case of video frame cutting, frame averaging, and frame exchanging. The poor imperceptibility of the watermark is still an issue to be resolved. Bitstream-based methods are mainly concerned with re-compression and re-encoding. Most of the proposed bitstream-based methods can resist re-compression and re-encoding, but they are generally not robust against geometric attacks and temporal synchronization attacks. Most of the video watermarking methods based on 3D and VR can well resist compression attacks and noise attacks, but they are generally not robust against geometric attacks such as rotation and cropping. This research has illustrated several aspects that should be considered in the future video watermarking research. For instance, the video watermarking method based on deep learning is in its intial stage and need to be improved. Temporal synchronization attacks and extended attacks have to be concerned consistently. Video watermarking application to more different forms of video signals should be resolved further.