# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 1 Introduction  
Nowadays, the issue of securing ownership rights to materials in digital form is one that requires further development. There are various ways of securing digital materials, such as DRM (Digital Rights Management), to protect against copying or illegal processing. A popular method of securing the copyright of video materials is to embed a watermark in them. The embedded watermark may or may not be visible. In the first case, a visible watermark is added to the video, often in the form of the logo of the TV station. The visible watermark has a high degree of ease of implementation and recognition, but it also obscures a portion of the video, altering the viewer's visual experience [1, 2]. In the case of transparent watermark embedding, no additional graphic element is introduced in the form of a visible watermark and, what is more, the person reproducing the material in question may not know that it is protected in this way due to its invisibility [3, 4]. Watermarking methods have also evolved with the development of their detection and removal [5, 6, 7, 8]. These methods address the detection and removal of visible as well as transparent watermarks. The implementation of embedding a visible watermark involves permanently adding a visible logo to the video, identifying the owner of the copyright to the video in question. On the other hand, the implementation of a transparent watermark is a more complex issue requiring consideration of fundamental issues related to the subject, such as capacity, visibility, resistance to changes of the carrier, and reversibility of changes made to the image. This publication will present a system for protecting ownership of video materials based on embedding a transparent watermark in the video. In the case of transparent imagewatermarking, there are descriptions in the literature of more elementary methods, such as the use of the least significant bit [9], based on frequency analysis, such as the wavelet transform [10], as well as more complex methods [11] that are a specific combination of basic methods or even dedicated to particular video codecs and use their characteristics for their operation. With the popularization of artificial neural networks (ANNs), watermark embedding methods using them have also emerged [12; 13; 14; 15; 16; 17]. Classical methods, as well as those based on ANNs, have strengths and weaknesses due to the characteristics of their operation. Both classical and ANNs-based methods have the potential for further development to improve the performance of existing methods or adapt them to new issues, such as new video codecs. The method presented in this article is based on the use of deep neural networks (DNNs) because with the right choice of DNN architecture and proper design of the training set, trained DNN should have the ability to find properties of the problem being analyzed that may not be included or predicted in a manually written algorithm. The aim of this article is to present an improved method compared to the previously published method for embedding a watermark in a high-quality video based on DNN under High-Efficiency Video Coding (HEVC) [18; 19; 20; 21] codec compression conditions [22]. The main research problem was to ensure the resistance of the watermark under higher HEVC compression levels than in a previous paper [22] and in comparison to other methods. Additionally higher capacity size for the watermark was achieved while providing higher values of carrier PSNR. The presented method of embedding the watermark takes into account the subsampling of the YUV420p chrominance in a way that ensures its recovery while providing high accuracy of the video with the embedded watermark in relation to the original video. The method is based on the use of a DNN autoencoder (using a real HEVC compression channel and mimicked by a trained coder in the training process) and on the use of an adjustable subsquares properties algorithm (ASPA) [22] method for watermark generation in chrominance channels.