Statistical Assessment of Cryptosystems for Image Uniformity, Correlation, and Randomness by@multithreading
Statistical Assessment of Cryptosystems for Image Uniformity, Correlation, and Randomness
by MultiThreading.Tech: #1 Publication on Concurrent ProgrammingMarch 15th, 2024
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The article conducts a thorough statistical evaluation of cryptosystems for real-time video encryption, focusing on uniformity, correlation, and randomness properties. Detailed analyses and results shed light on the effectiveness of these systems in enhancing data security and resisting statistical attacks.
Authors:
(1) Dong Jiang, School of Internet, Anhui University, National Engineering Research Center of Agro-Ecological Big Data Analysis and Application, Anhui University & [email protected];
(2) Zhen Yuan, School of Internet, Anhui University;
(3) Wen-xin Li, School of Internet, Anhui University;
(4) Liang-liang Lu, Key Laboratory of Optoelectronic Technology of Jiangsu Province, Nanjing Normal University, National Laboratory of Solid State Microstructures, Nanjing University, Nanjing & [email protected].
Table of Links
4. Statistical Evaluation
The essence of real-time video encryption is image encryption, which should provide excellent statistical properties to resist different types of attacks [28]. In this section, therefore, the uniformity, correlation, and randomness of the deployed cryptosystems are analyzed. The following experiments are performed using the laptop (Intel Core i5-1135G7), and the parameter setup are as follows: number of assistant threads: n = 8, confusion and diffusion rounds r = 5, the size of all plain images used for statistical evaluation is 512 × 512.
4.1. Uniformity Evaluation
The histogram, variances and χ 2 of histograms are utilized to evaluate the uniformity of the deployed cryptosystems. The plain image is plotted in Fig. 3 (a), and its histograms of red, green, blue channels are shown in 3 (b), (c), (d), respectively. The cipher image encrypted with PLCM is shown in Fig. 3 (e). Its histograms of red, green, blue channels are shown in Fig. 3 (f), (g), (h), respectively. The cipher image encrypted with 2DLASM is shown in Fig. 3 (f). Its histograms of red, green, blue channels are shown in Fig. 3 (j), (k), (l), respectively.
4.2. Correlation Evaluation
Since two adjacent pixels in an image usually have high correlation, the cryptosystems should decrease the correlation to resist statistical attacks [32]. Therefore, the correlation between two adjacent pixels in plain and cipher images are analyzed. We first randomly select 6000 pairs of two horizontally, vertically, and diagonally adjacent pixels from the plain and cipher images. The correlation distribution of red, green, and blue channels in plain image shown in Fig. 4 (a), (b), (c), respectively. The correlation distribution of red, green, and blue channels in cipher image are plotted in Fig. 4 (d), (e), (f), respectively. The correlation distribution of red, green, and blue channels in cipher image shown in Fig. 3 (i) are drawn in Fig. 4 (g), (h), (i), respectively.
When the correlation coefficient of a cipher image is lower, the resistance capability of the encryption algorithm to statistical analysis attacks is stronger. To assess the correlation property of the proposed strategy, we encrypt a set of plain images using PLCM and 2DLASM, randomly select 20000 adjacent pixels in horizontal, vertical, and diagonal directions from the plain and cipher images, calculate the correlation coefficients, and list the results in Tab. 4. The deployed cryptosystesm, clearly, significantly decrease the correlation of the plain images.
4.3. Randomness Evaluation
This paper is available on arxiv under CC 4.0 license.
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