## 3 Related Works
The aim of the work [1] is to evaluate the effectiveness of the usage of stylometry as an alternative method of authentication of users of information systems. The authors suggested to use the descriptions of the images from users as a backup authentication system. The users were asked to describe the photos on four topics: ocean, desert, sky, and green landscapes. For the attribution of users, their writing styles are determined, which include vocabulary selection, phrasal pauses, word selection, and other features. All descriptions must be at least 200 words long. If the user forgets the account data, the backup authentication system provides another image containing the same thematic data. The new and previous descriptions are presented using a bag-of-words (BOW), after which the feature vectors are compared using the cosine similarity. If the score is above the threshold level, the author of the text will be authenticated. The model based on the support vector machine method was also built. The control experiment demonstrated an accuracy of about 73%. The article [18] examines the approaches to determining the authorship of texts in Russian, as well as the advantages and disadvantages of these approaches. The article describes the attribution of texts, the sizes of which vary from 1000 to 100,000 symbols, using the method of support vectors (SVM) and deep neural networks with long short-term memory (LSTM), the convolutional neural networks (CNN), and transformer networks. The results show that all the considered algorithms are suitable for solving the problem of authorship identification, but SVM shows the best accuracy. The average accuracy of the SVM reaches 96%, while the accuracy increases with increasing text size and decreases with an increasing number of authors under consideration (the number of authors in the sample varied from 2 to 50). The high accuracy is due to the carefully selected parameters of the models and the feature space, which includes statistical and semantic features, among them those which are extracted as a result of the aspect analysis. Deep neural networks are inferior to SVM in accuracy and reach only 93%. The experiments show that SVM-based methods are unstable to deliberate text anonymization. Some authors suggest to use the pre-trained language models for stylometry. The initialization of word vectors is carried out using the Word2 vec model, this approach is a popular method of increasing the productivity in the absence of a large training set of texts. The work [19] describes a series of experiments with convolutional neural networks (CNN) and trained word vectors based on the Word2 vec model for sentence level classification tasks. The vectors have a dimension of 300 and were trained using the continuous bag-of-words architecture. The words missing from the set of pre-trained words were initialized randomly. The authors show that a relatively simple CNN network with a small adjustment of hyperparameters and with the word vectors based on the pre-trained Word2 vec model gives excellent results on a number of tests, for example, for tonality analysis tasks. The authors of [20] used the Doc2 vec model from the GENSIM3 library for cross-thematic attribution of articles by 15 authors from _The Guardian_ newspaper in English. The description of the texts included \(n\)-grams of symbols, \(n\)-grams of words, and \(n\)-grams of tags of parts of speech; in all cases, \(n\) varied from 1 to 5. After the presentation of the training data in the form of \(n\)-grams, Doc2 vec was applied to them to obtain a numerical description of the training documents. The Doc2 vec model allows to represent the documents in the form of continuous and dense fixed-length feature vectors. This model generates a vector representation at the document level, it is implemented by processing each document as a special word. Thus, the authors of the work describe both the syntax and the semantics of documents, what ensures the high accuracy in attribution tasks. The logistic regression was used as a classifier.