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S-Box-based video stenography application of variable-order fractional hopfield neural network (VFHNN)

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dc.contributor.authors Cavusoglu, Unal
dc.date.accessioned 2022-12-20T13:25:49Z
dc.date.available 2022-12-20T13:25:49Z
dc.date.issued 2022
dc.identifier.issn 1951-6355
dc.identifier.uri http://dx.doi.org/10.1140/epjs/s11734-022-00449-1
dc.identifier.uri https://hdl.handle.net/20.500.12619/99453
dc.description Bu yayının lisans anlaşması koşulları tam metin açık erişimine izin vermemektedir.
dc.description.abstract With the increasing usage of the Internet nowadays, information security has become critical. Data hiding strategies are one approach used to ensure information security. Many different methods for data hiding are reported in the literature. In this study, an substitution-box (S-Box) based video stenography algorithm is suggested. In the design of the developed system, the variable-order fractional hopfield neural network (VFHNN) chaotic system is used and the analysis of the system is carried out. A random number generation algorithm is designed using the VFHNN system, and the randomness of the generated numbers is tested with NIST-800-22 tests. A new S-Box generation algorithm has been developed using the numbers obtained from the random number generator. Performance tests are applied to test the cryptological robustness of the produced S-Boxes. Then, the proposed algorithm is introduced to provide high-capacity and secure data hiding processes. Using four different S-Boxes, random determination of the pixels on the video file that will be data hiding is performed. To determine the security and performance of the proposed algorithm, data hiding processes are performed with different video files and analyses are carried out. The results obtained are compared with the studies in the literature, and it is shown that the proposed system achieved high-capacity and secure data hiding.
dc.language English
dc.language.iso eng
dc.relation.isversionof 10.1140/epjs/s11734-022-00449-1
dc.subject Physics
dc.title S-Box-based video stenography application of variable-order fractional hopfield neural network (VFHNN)
dc.identifier.volume 231
dc.identifier.startpage 2017
dc.identifier.endpage 2035
dc.relation.journal EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
dc.identifier.issue 10
dc.identifier.doi 10.1140/epjs/s11734-022-00449-1
dc.identifier.eissn 1951-6401
dc.contributor.author Cavusoglu, Unal
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı


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