Detecting audio steganography and signs of modification in the data of audio files

Abstract

The most commonly used and transferred files are audio files. However, when transferring such files, we face the problem of detecting any modifications. The widespread use of sound editors and various programmes for the processing and editing of audio files makes it possible for anyone to falsify speech phonograms. Another type of modification involves using audio files as steganographic containers. There are a large number of steganography techniques for the covert transmission of information within standard files. In this regard, it is important to develop analytical tools and methods for the detection of modifications in audio files. There fore, the article presents a study on the detection of hidden steganographic messages and signs of modifications in the data area of audio files based on determining the numerical criteria for decision making. The article considers methods for the statistical analysis of audio files for any modifications in the data. When analysing the alterations made to audio files, steganalysis methods and spectral analysis were used. The article presents algorithms based on the assessment of changes in the statistical characteristics of the signal-to-noise ratio, which make it possible to evaluate the modifications. Algorithms for calculating the coefficient of the signal-to-noise ratio are described both for separate blocks and for the whole file. The implemented algorithms were tested on audio files, which were preliminary modified in the following ways: embedding — using the audio file as a stegocontainer; merging - using two audio files to make one; clipping — modifying the audio file by deleting some of the information. Using the proposed methods, we evaluated the possibility of modifications made to the files. The experiment demonstrated that the presented methods allow the detection of merged audio files and removed fragments without using the original. The article provides data that shows the accuracy of modification detection.