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Substitution box is the most significant component of block cipher. The property of nonlinearity is of great importance for the design of secure substitution boxes. Therefore, it is necessary to develop new methods for the production of substitution boxes with a high non-linearity score. In this paper, we have introduced a novel group theoretic method to construct a robust S-box with non-linearity score 113.75 greater than that of AES S-box. The performance of generated S-box is found to be excellent, when examined through various other well-known algebraic criteria such as strict avalanche criterion, bit independence criterion, differential uniformity and linear approximation probability. The suitability of proposed S-box is tested for image encryption applications through different statistical analyses. We got very encouraging outcomes from all these examinations which certify that the generated S-box meets all the criteria needed to be reliable for secure communication and image encryption.
相似文献By analyzing the essence and deficiency of the improved Otsu’s method, this paper proposes a noise adaptive angle threshold based Otsu’s method for gesture image segmentation. It first designs a two-dimensional histogram of gray value-neighborhood truncated gray mean to avoid the interference of extreme noise by discarding the extremes of the neighborhood. Then, the probability that the pixel is noise is calculated according to the actual situation, adaptive filtering is implemented to enhance the algorithm’s universal applicability. It finally converts the threshold space to an angle space from 0° to 90°, and the threshold search range is compressed to improve its efficiency. As the gesture is close to the background and the boundary is blurred, this paper combines the global and local Otsu’s method to segment the gesture images based on the angle space. On the one hand, it uses the global Otsu’s method to obtain the global threshold t1. On the other hand, it uses the local Otsu’s method to obtain the local threshold t2, and segments gesture images based on t2. Experimental results show that the proposed method is effective and can accurately segment gesture images with different noises.
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