Statistical stegdetectors performance by message re-embedding

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dc.contributor.authorProgonov, Dmytro
dc.date.accessioned2023-05-11T10:50:13Z
dc.date.available2023-05-11T10:50:13Z
dc.date.issued2021
dc.description.abstractState-of-the-art stegdetectors for digital images are based on pre-processing (calibration) of analyzed image for increasing stego-to-cover ratio. In most cases, the calibration is realized by image processing with enormous set of high-pass filters to obtain good estimation of cover image from the stego one. Nevertheless, the efficiency of this approach significantly depends on careful selection of filters for reliably extraction of cover image alterations that are specific for each embedding method. The selection is non-trivial and laborious operation that is realized today by training of convolutional neural networks, such as Ye-Net, SR-Net to name but a few. The paper is devoted to performance analysis of alternative approach to image calibration, namely message re-embedding into analyzed image. The considered method is aimed to increasing stego-to-cover ratio by amplification of cover image alterations caused by message hiding. The analysis was performed on ALASKA and VISION datasets by usage of stegdetector based on SPAM model of covers. Messages were re-embedded according to state-of-the-art adaptive methods HUGO, S-UNIWARD, MG and MiPOD. Proposed approach allows significantly (up to 20%) decreasing detection error even in case of low payload of cover image (less than 10%) where modern stegdetectors are ineffective.uk
dc.format.pagerangePp. 5-14uk
dc.identifier.citationProgonov, D. Statistical stegdetectors performance by message re-embedding / Dmytro Progonov // Theoretical and Applied Cybersecurity : scientific journal. – 2021. – Vol. 3, Iss. 1. – Pp. 5–14. – Bibliogr.: 21 ref.uk
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132021.1.251291
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/55576
dc.language.isoukuk
dc.publisherIgor Sikorsky Kyiv Polytechnic Instituteuk
dc.publisher.placeKyivuk
dc.relation.ispartofTheoretical and Applied Cybersecurity: scientific journal, Vol. 3, No. 1uk
dc.subjectdigital image steganalysisuk
dc.subjectadaptive embedding methodsuk
dc.subjectmessage re-embeddinguk
dc.subject.udc004.[056.54:932.2]uk
dc.titleStatistical stegdetectors performance by message re-embeddinguk
dc.typeArticleuk

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 3, No. 1