The paper provides a survey of foreign studies regarding steganalysis, aimed to detect hidden message insertion made by applying least significant bit replacement and discrete cosine transform algorithms. It is noted the further steganalysis methodology development splits in two directions: a decrease of the complexity and cost of processing and detection while maintaining a high level of classification rate, which is quite justified in the case of the presence of insertions with a large payload, i.e. up to 100%; or an increase of the insert recognition efficiency when dealing with images of a low payload. Besides, during the last five years, steganalisys methods based on machine learning and deep learning began to play a dominant role in steganalysis
In the paper, a new software package is presented as a tool that allows to conduct steganalysis of color images and reveal embedded messages such as LSB inserts or Koha-Zhao inserts. The complex combines two algorithms previously developed by the authors and is highly effective even for small stegocontainer payload (10–30 %). The computer experiment results and a comparison of the software package and some other models performance are presented and discussed.”.
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