The efficiency assessment of using hybrid neural networks for the detection of forged audio data in socially oriented systems

The subject of the research is the problem of detecting falsified data, in particular in audio format, in socially oriented systems. The goal of the work is to develop an effective model based on recurrent and convolutional neural networks for determining the fact of forgery of sound data, using MapReduce technology for parallelization. The article addresses the following tasks: determining the features of audio in socially-oriented systems, conducting an analysis of algorithms for processing audio information both in the form of text and in the form of a signal, forming a list of target architectures of neural networks and revealing the features of their implementation, conducting an experimental test of effectiveness selected approaches. The following methods used are – analytical and inductive method for determining the target set of neural network architectures; expert assessment for the formation of the most influential efficiency factors; experimental, multi-criteria evaluation and statistical methods of data augmentation to determine the most effective model. The following results were obtained: an audio data reprocessing algorithm was developed for the possibility of using recurrent and convolutional networks. Several approaches to data classification using augmentation based on vector autoregression and MapReduce parallelization technology have been implemented. It was determined that the most effective model for the multi-criteria selection problem is a combination of a bidirectional recurrent neural network with support for short- and long-term memory with several convolutional networks. The advantages of using MapReduce technology to optimize training time and data processing are shown, and a set of open questions for further research and applied implementation is defined. Conclusions: he application of an analytical and inductive approach followed by experimental verification made it possible to develop an effective (with an accuracy of more than 96%) a mechanism for detecting fabricated data both in the form of a signal and in text form. The obtained result makes it possible to assert the feasibility of implementing the proposed approach, and, accordingly, makes it possible to reduce the influence of such information in socially oriented systems, especially during crisis events.