https://doi.org/10.1140/epjs/s11734-022-00449-1
Regular Article
S-Box-based video stenography application of variable-order fractional hopfield neural network (VFHNN)
Software Engineering Department, Faculty of Computer and Information Sciences, Sakarya University, Serdivan, Turkey
Received:
29
August
2021
Accepted:
13
January
2022
Published online:
28
January
2022
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.
© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022
