## 1 Introduction  
The issue of copyright protection is a multi-billion-dollar problem affecting both developed and developing countries. Related to this is the phenomenon of multimedia piracy, i.e., the unauthorized distribution and redistribution of multimedia content such as films, TV programs, or audio files [1]. One of the main drivers of this phenomenon is the desire of consumers to watch new content as soon as it is released, without having to pay for premium TV services. The most common method for the illegal use of films and TV series is through stream ripping, while many consumers also illegally download and stream content [2]. Multimedia piracy is a source of huge financial losses for both owners and distributors of entertainment content and for consumers, as it is the source of many restrictions and limitations on offering entertainment to end customers [3]. The solution to the problem is to embed an invisible, capacious, and robust watermark in the media content, allowing the owner of the content to be identified and the source of the data leak to be traced in the event of an unauthorized distribution occurring. Digital Rights Management aims to develop systems to counteract the use of digital data in a manner contrary to the will of the publisher. Digital watermarking is very often used in this area [4; 5; 6]. A watermark embedded in the video content should comply with three basic paradigms to implement the method in commercial applications:  
* Transparency, i.e., the invisibility of the watermark to the human visual system (HVS) [7]. The video viewer usually does not have access to the original video (without the watermark), so seeing minor modifications is impossible, but despite this, the watermark may not significantly affect the quality of the video, which is verified in a measurable way based on metrics [8]. The bit capacity--the number of watermark bits that can be encoded in one video frame--should be as high as possible to encode as much data as possible. Information mappers are used to transform the binary signature into a watermark [9; 10]. The robustness of the algorithm is determined by its ability to decode the correct watermark from the material. Measurably, robustness is assessed based on the BER (bit error rate) metric by comparing the original watermark and the watermark extracted from the video [11]. The area of watermark embedding and extraction in video files has so far been dominated by three main approaches--watermark embedding in the spatial domain [12; 13; 14]; the transform domain with the Discrete Cosine Transform (DCT) [15], Discrete Wavelet Transform [16], Discrete Fourier Transform [17], or combined methods [18; 19]; and the compression domain [20; 21]. Currently, classical methods are increasingly being supported or replaced by the use of deep neural network algorithms [22], which have found applications in many areas, both civil and military [23]. Algorithms using neural networks are characterized by much higher efficiency, but also high computational complexity, which significantly increases the processing time of a single frame for watermark embedding [24]. This paper presents a modification of an algorithm using artificial neural network architectures to embed a static image in a luminance channel in the YUV color space with high transparency preserved. Modifications were made to the loss function and the model training process, and the architecture was adapted to handle five-dimensional input data (video sequences). In addition, an information mapper and a demapper have been implemented to allow high-capacity binary signatures to be encoded in the video frame. The main research problem addressed in the study was the selection of the algorithm's hyperparameters to achieve the highest possible binary watermark capacity while maintaining high transparency and robustness.