Abstract With the development of the Internet, image encryption technology has become critical for network security. Traditional methods often suffer from issues such as insufficient chaos, low randomness in key generation, and poor encryption efficiency. To enhance performance, this paper proposes a new encryption algorithm designed to optimize parallel processing and adapt to images of varying sizes and colors. The method begins by using SHA-384 to extract the hash value of the plaintext image, which is then processed to determine the chaotic system's initial value and block size. The image is padded and divided into blocks for further processing. A novel two-dimensional infinite collapses hyperchaotic map (2D-ICHM) is employed to generate the intra-block scrambling sequence, while an improved variable Joseph traversal sequence is used for inter-block scrambling. After removing the padding, 3D forward and backward shift diffusions, controlled by the 2D-ICHM sequences, are applied to the scrambled image, producing the ciphertext. Simulation results demonstrate that the proposed algorithm outperforms others in terms of entropy, anti-noise resilience, correlation coefficient, robustness, and encryption efficiency.
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 62105004 and 52174141), the College Student Innovation and Entrepreneurship Fund Project (Grant No. 202210361053), Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center, Anhui University of Science & Technology (Grant No. KSJD202304), the Anhui Province Digital Agricultural Engineering Technology Research Center Open Project (Grant No. AHSZNYGC-ZXKF021), the Talent Recruitment Special Fund of Anhui University of Science and Technology (Grant No. 2024yjrc175), the Graduate Innovation Fund Project of Anhui University of Science and Technology (Grant Nos. 2024cx2067, 2024cx2107, and 2024cx2064), and Seed Support Project for Postgraduate Innovation, Entrepreneurship and Practice at Anhui University of Science and Technology (Grant No. 2024cxcysj084).
Yan Hong(洪炎), Xinyan Duan(段心妍), Jingming Su(苏静明), Zhaopan Wang(王昭盼), and Shihui Fang(方士辉) A novel approach to visual image encryption: 2D hyperchaos, variable Josephus, and 3D diffusion 2025 Chin. Phys. B 34 040504
[1] Wang X and Zhao M 2021 Opt. Laser Technol. 143 107316 [2] Zhu S and Zhu C 2024 Sci. Rep. 14 15496 [3] Alexan W, El-Damak D and Gabr M 2024 IEEE Access 12 21092 [4] Gabr M, Elias R, Hosny K M, Papakostas G A and Alexan W 2023 IEEE Access 11 85002 [5] Lai Q, Hua H, Zhao X W, Erkan U and Toktas A 2023 Chaos Soliton. Fract. 175 114022 [6] Gabr M, Korayem Y, Chen Y L, Yee P L, Ku C S and Alexan W 2023 IEEE Access 11 119284 [7] AlexanW, Aly L, Korayem Y, Gabr M, El-Damak D and Fathy A 2024 IEEE Access 12 78589 [8] Alexan W, Gabr M, Mamdouh E, Elias R and Aboshousha 2023 IEEE Access 11 54928 [9] Lai Q and Chen Z 2023 Chaos Soliton. Fract. 176 114118 [10] Shakiba A 2021 Multimed Tools Appl. 80 17983 [11] Wu X, Wang D, Kurths J and Kan H 2016 Inf. Sci. 349 137 [12] Ding Y, Duan Z and Li S 2023 Vis. Comput. 39 1517 [13] Teng L, Wang X, Yang F and Xian Y 2021 Nonlinear Dyn. 105 1859 [14] Lai Q and Liu Y 2023 Expert Syst. Appl. 223 119923 [15] Zhou N R, Tong L J and ZoubWP 2023 Signal Processing 211 109107 [16] Gong L H and Luo H X 2023 Optics & Laser Technology 167 109665 [17] Guan Z, Li J, Huang L, Xiong X, Liu Y and Cai S 2022 Entropy 24 384 [18] Alkhayyat A, Ahmad M, Tsafack N, Tanveer M, Jiang D and Abd El-Latif A A 2022 J. Sign. Process. Syst. 94 315 [19] Le Z, Li Q, Chen H, et al. 2024 Phys. Scr. 99 055249 [20] Chen X, Mou J, Cao Y and Banerjee S 2023 Int. J. Bifurcation Chaos 33 2350190 [21] Hosny K M, Kamal S T and Darwish M M 2022 Multimed. Tools Appl. 81 505 [22] Wang X, Zhao M, Feng S and Chen X 2023 Soft Comput. 27 1223 [23] Kumar B S and Revathi R 2024 J. Eng. Appl. Sci. 71 41 [24] Guo Z, Chen S H, Zhou L and Gong L H 2024 Appl. Math. Mod. 131 49 [25] He D, He C, Jiang L G, Zhu H W and Hu G R 2001 IEEE Trans. Circuits Syst. 48 900 [26] Su J, Hong, Y, Fang S and Wen Y 2024 Chin. Phys. B 33 070502 [27] Lai Q and Yuan L 2023 Expert Systems with Applications. 223 119923 [28] Wen J, Xu X and Sun K 2023 Nonlinear Dyn. 111 6813 [29] Wang X, Xu X and Sun K 2023 Nonlinear Dyn. 111 14513 [30] Zhou N R, Hu L L, Huang Z W, Wang M M and Luo G S 2024 Expert Syst. Appl. 238 122052 [31] Nardo L G, Nepomuceno E G, Arias-Garcia J, et al. 2019 Chaos Soliton. Fract. 123 69 [32] Chen C, Sun K H and He S 2020 Signal Process. 168 107340 [33] Pak C, Sun K H and He S B 2020 Signal Process. 168 107340 [34] Li Z, Peng C, Tan W and Li L 2020 Symmetry 12 1497 [35] Zhang Y Q, He Y, Li P and Wang X Y 2020 Opt. Laser Technol. 128 106040 [36] Liu X, Tong X, Wang Z and Zhang M 2022 Multimed. Tools Appl. 81 21779 [37] Lai Q, Hu G, Erkan U and Toktas A 2023 Expert Syst. Appl. 213 118845 [38] Wen Y, Su J, Hong Y and Gong P 2022 IET Image Process 16 2467 [39] Lai Q and Hu G 2024 Transactions on Industrial Informatics 20 11262
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
