Analysis of S-Box Based on Image Encryption Application Using Complex Fuzzy Credibility Frank Aggregation Operators

Abstract

This article is about a criterion based on credibility complex fuzzy set (CCFS) to study the prevailing substitution boxes (S-box) and learn their properties to find out their suitability in image encryption applications. Also these criterion has its own properties which is discussed in detailed and on the basis of these properties we have to find the best optimal results and decide the suitability of an S-box to image encryption applications. S-box is the only components which produces the confusion in the every block cipher in the formation of image encryption. So, for this first we have to convert the matrix having colour image using the nonlinear components and also using the proposed algebraic structure of credibility complex fuzzy set to find the best S-box for image encryption based on its criterion. The analyses show that the readings of GRAY S-box is very good for image data. Contributions: As from literaure reveiw a FS contained one MD and IFS conatined two degrees which is MD and NMD. As these generalization of crisp set there is no detailed about the degree of accuarcy (credibility). So, here in this paper we have used the CFCS information which has MD and degree of credibility (accuracy) as well as we have develop a series of aggregation operators which is based on the Frank norms. And also we have discussed CoCoSo method under CFCS information.

An Efficient Substitution Box design with a chaotic logistic map and Linear Congruential Generator for secure communication in Smart cities

“…The map used (in this method) is based on simple programming and does not have solid mathematical roots. Using credibility complex fuzzy sets (CCFS), Yahya et al [28] proposed a novel scheme for designing an S-box for the encryption of images and discussed the results for the suitability of the proposed S-box for image encryption.…”

Analysis and Applications of Artificial Intelligence in Digital Education Based on Complex Fuzzy Clustering Algorithms

“…Furthermore, we can easily derive two different types of results by using the information in Equation (15); for instance, by putting the value of Φ = 0, 1, we obtain…”

“…By putting 18), we obtain the notion given in Equation (15). Moreover, from the information in Equations ( 15) and ( 18), we can easily derive the theory of distance measures such as…”

“…In addition, Xuecheng [11] established the three varying categories of methodology, such as entropy, distance, and SMs based on fuzzy information, while Beg and Ashraf [13] outlined the SMs for FSs, and Chen et al [14] considered a multitude of SMs based on fuzzy information. Moreover, the work of Couso et al [15] resulted in the theory of SMs and dis-SMs with their existing knowledge of fuzzy set theory, whereas Zhang and Fu [16] showcased their SMs based on FSs. Guo et al [17], however, focused on the theory of cosine SMs based on CFS, while Hu et al [18] evaluated the distance, similarity, and continuity information for CFS.…”

“…Following on from this, the theory of a complex multi-fuzzy set was discovered by A-Qudah and Hassan [11], while Tamir et al [12] created the theory of similarity measures reliant on complex fuzzy logic and CFSs. Moreover, Ghorbani et al [13] exposed the semantic interoperability based on type-2 fuzzy sets; Jan et al [14] evaluated the evaluation of digital systems for complex fuzzy soft information and their applications; Yahya et al [15] derived the S-box based on image encryption for complex fuzzy frank operators; and Zeeshan et al [16] evaluated the distance function for complex fuzzy soft information and their application in signals.…”