Analysis of chi-squared divergence changes by filtering of stego images formed according to UNIWARD embedding methods
DOI:
https://doi.org/10.20535/RADAP.2019.76.72-76Keywords:
steganalysis, adaptive embedding methods, UNIWARD algorithm, chi-squared divergenceAbstract
Counteraction to sensitive information leakage is topical task today. Special interest is taken on early detection of hidden (steganographic) information transferring by data transmission in communication systems. Message (stego data) embedding is provided by alteration of cover files, such as digital images, according to used steganographic algorithm. Reliable detection of formed stego images requires usage of targeted stegdetector that needs a priori information about specific distortions (signatures) of cover due to data hiding. It makes detection systems vulnerable to zero-day attack – usage by malefactors the previously unknown embedding algorithms. Therefore it is required development of universal (blind) stegdetectors that are capable to reliable revealing of stego images even in case of limited or absence information about used embedding method. Creation of blind stegdetector requires determination of cover image parameters that are sensitive to any alteration caused by message hiding. As such parameters it is proposed to use information-theoretic estimations (chi-square divergence) of pixels brightness distribution distortion due to stego data embedding. For amplification of these distortions it is used image pre-processing with median and Wiener filters. The case of adaptive messages hiding in cover images according to UNIWARD methods is considered. It is revealed that usage of chi-square divergence allows reliably detection of small alteration of cover image even in case of low cover payload (less than 10\%). Different character of chi-square divergence changes for filtered images by information hiding in spatial and JPEG domains allows determine type of used embedding domain.Downloads
Published
2019-03-30
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Section
Information Security
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Copyright (c) 2019 D. O. Progonov
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