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Article

Нейросетевой стегоанализ аудиоданных файлов в МР3-формате

Драчев Г. А., Истратов А. Ю.

An approach to the detection of hidden information (stegocontainers) in the audio data of MP3 files based on neural network modeling is considered. A multilayer perceptron is used as the instrumental model of the neural network.
The structural components of the MP3 file are analyzed: fields containing related information (song titles, album, information about the author, lyrics, etc.), and frames, and fragmented sets of encoded audio data. Useful data are highlighted. A procedure is proposed for presenting audio data of any MP3 file as a uniform set of features of a relatively small size. The dimension of the feature set (data set) can be selected from the range [100-520], in accordance with the minimum and maximum frame size, depending on the compression quality of a single audio file when encoded in MP3 format.
Modern software packages for encrypting and decrypting stegocontainers into MP3 files are being investigated. Based on selected software implementations, a database of examples (data sets) is formed from pre-processed MP3 files both containing the stegocontainer and without the stegocontainer. The structure of the neural network for steganalysis of MP3 files is determined experimentally, it is trained and tested. The test results of the neural network system allow us to state its high efficiency