## 3 Proposed Method  
### Presentation of the Concept of the Proposed Method  
The watermark embedding method proposed in this paper for H.265/HEVC (High-Efficiency Video Coding) encoded video is based on DNNs and an adjustable subsquares properties algorithm. The research was conducted on a Linux operating system using the TensorFlow version 2.7 machine learning library. The FFmpeg library was used to modify the video material, while the source codes were written in Python version 3.9. The following is the concept underlying the method, which is based on an article by [PERSON] [44]. The article deals with hiding a static image in another static image. The method is based on the use of three ANNs (preliminary networks which prepare the hidden image, encode it, and decode it) as components that form a system for hiding the image and recovering it, the process for which is presented in Figure 1.  
The method, presented hereafter, employs a modified version of the general system scheme, incorporating an additional preliminary network at the decoder input as well as at the encoder and decoder subsystems. The general conceptual scheme of the system components is shown in Figure 2; however, it is worth noting that this is a superficial representation of the system. The individual components presented in the diagram from the paper and in the diagram created from the actually conducted research are not the same, and they differ in their complexity. Figure 1: General conceptual diagram of the system components from the foundational article [44]. At the very beginning, the encoder subsystem prepares a hidden image, which is a watermark, in accordance with the adjustable subsquares properties algorithm presented in the foundational article. Then, the hidden image is fed into the input of the preliminary network. The pre-prepared, hidden image, together with the original image, which is a carrier for the hidden image, is fed into the input of the encoding network. The encoder DNN substructure encodes the watermark in the carrier image by decomposing the secret image from the preliminary network to the set of features. The next step is to carry out the deconvolution of the obtained set of secret image features with the carrier image (achieved via the substructure of the encoder so as to achieve an encoded image carrier with the watermark) which embeds the watermark optimally so as to preserve the image as accurately as possible, as compared to the original image, while ensuring the possibility of recovering the watermark. During the learning process, the neural network automatically selects (learns) the optimal filters (of the convolutional layers) by which the image modification can be carried out in order to achieve the assumed goal. The encoding of the data bits in the image is performed by encoding the bit data with the adjustable subsquares properties algorithm and obtaining a bit-encoded image. In the next step, the DNN encoder performs the optimal image encoding. The entire carrier image is subject to permanent modification because the watermark is embedded in the entire image area that is serviced by the encoder. After the image has passed through the compression channel of the HEVC codec, the decoding process begins. In the decoding process, the watermark image, which was created as a result of the encoding process, is fed into the input of the preliminary network of the decoder, and then the prepared image is fed into the input of the decoding network. The DNN decoder is responsible for the retrieval of the watermark from the carrier image.