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Про підвищення точності виявлення JPHIDE стеганограм: Fìz.-mat. model. ìnf. tehnol. 2021, 32:170-174

The paper proposes a method for improving the accuracy of steganoanalytical systems that use an ensemble classifier. The method involves a weighted final vote of several highly sensitive models of characteristic vectors. Its effectiveness was evaluated for the task of detecting steganograms created...

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Datum:2021
Автори та афіліації:
  • Nataliia Koshkina — Інститут кібернетики імені В.М. Глушкова НАН України, м. Київ
Ключові слова:keywords
1. Verfasser: Koshkina, Nataliia
Format: Artikel
Sprache:Ukrainisch
Veröffentlicht: Інститут прикладних проблем механіки і математики ім. Я. С. Підстригача НАН України 2021
Schlagworte:
інформаційна безпека
стеганографія
стеганоаналіз
інтелектуальні комп’ютерні системи
машинне навчання
точність детектування
Online Zugang:https://www.fmmit.lviv.ua/index.php/fmmit/article/view/181
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Назва журналу:Physico-mathematical modeling and informational technologies

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Physico-mathematical modeling and informational technologies
Beschreibung
Zusammenfassung:The paper proposes a method for improving the accuracy of steganoanalytical systems that use an ensemble classifier. The method involves a weighted final vote of several highly sensitive models of characteristic vectors. Its effectiveness was evaluated for the task of detecting steganograms created by the Jphide program. The accuracy obtained by usage of one of the models: LIU, CC-PEV, CC-C300, DCTR, PHARM, GFR and with using a combination of several models according to the developed method was compared. The test results proved that the weighted final voting of several highly sensitive models does increase the accuracy of the detection of steganograms with a relatively small payload (short secret messages) without compromising the accuracy of the detection of steganograms with a high payload. References Koshkina, N. V. (2020). Comparison of Efficiency of Statistical Models Used for Formation of Feature Vectors by JPEG Images Steganalysis. Theoretical and Applied Cybersecurity, 2(1), 22-28. DOI doi.org/10.20535/tacs.2664-29132020.1.209433 Koshkina, N. V. (2020). Research of Main Components of Machine Learning Based JPEG-Steganalysis Systems. Ukrainian Information Security Research Journal, 22(2), 97-108. http://jrnl.nau.edu.ua/index.php/ZI/article/view/14801/21490 Holub, V., Fridrich, J. (2015). Phase-Aware Projection Model for Steganalysis of JPEG Images, Proc. SPIE. Electronic Imaging, Media Watermarking, Security, and Forensics XVII. DOI doi.org/10.1117/12.2075239
DOI:10.15407/fmmit2021.32.170

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