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Quantum color image encryption: Dual scrambling scheme based on DNA codec and quantum Arnold transform |
Tao Cheng(程涛)2, Run-Sheng Zhao(赵润盛)1, Shuang Wang(王爽)2, Kehan Wang(王柯涵)1, and Hong-Yang Ma(马鸿洋)1,† |
1 School of Sciences, Qingdao University of Technology, Qingdao 266033, China; 2 School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China |
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Abstract In the field of Internet, an image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of international research. We combine DNA codec with quantum Arnold transform (QArT) to propose a new double encryption algorithm for quantum color images to improve the security and robustness of image encryption. First, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Second, we use QArT to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of "Monkey", "Flower", and "House". The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average normalized pixel change rate (NPCR) of RGB three-channel is 99.61%, the average uniform average change intensity (UACI) is 33.41%, and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.
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Received: 27 August 2024
Revised: 06 October 2024
Accepted manuscript online: 23 October 2024
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PACS:
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03.67.Ac
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(Quantum algorithms, protocols, and simulations)
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03.67.Lx
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(Quantum computation architectures and implementations)
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03.67.-a
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(Quantum information)
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Fund: Project supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2021MF049), Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos. ZR2022LLZ012 and ZR2021LLZ001), and the Key R&D Program of Shandong Province, China (Grant No. 2023CXGC010901). |
Corresponding Authors:
Hong-Yang Ma
E-mail: hongyang_ma@aliyun.com
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Cite this article:
Tao Cheng(程涛), Run-Sheng Zhao(赵润盛), Shuang Wang(王爽), Kehan Wang(王柯涵), and Hong-Yang Ma(马鸿洋) Quantum color image encryption: Dual scrambling scheme based on DNA codec and quantum Arnold transform 2025 Chin. Phys. B 34 010305
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[1] Nagarajan S M, Deverajan G G, Kumaran U, Thirunavukkarasan M, Alshehri M D and Alkhalaf S 2021 IEEE Transactions on Industrial Informatics 18 12 [2] Chen H C, Guo J I, Huang L C and Yen J C 2003 EURASIP Journal on Advances in Signal Processing 2003 902741 [3] Chinnasamy C 2018 Ingenierie des Systemes d’Information 23 6 [4] Pourasad Y, Ranjbarzadeh R and Mardani A 2021 Entropy 23 341 [5] Arab A, Rostami M J and Ghavami B 2019 The Journal of Supercomputing 75 6663 [6] Zhang Y 2020 Information Sciences 520 177 [7] Parvaz R and Zarebnia M 2018 Opt. Laser Technol. 101 30 [8] Xu Q, Sun K, He S and Zhu C 2020 Opt. Lasers Eng. 134 106178 [9] Wang Z, XuMand Zhang Y 2022 Arch. Comput. Methods Eng. 29 737 [10] Yao X W, Wang H, Liao Z, Chen M C, Pan J, Li J, Zhang K C, Lin X C, Wang Z H, Luo Z H, Zheng W Q, Li J D, Zhao M S, Peng X H and Suter D 2017 Phys. Rev. X 7 031041 [11] Cai Y, Lu X and Jiang N 2018 Chin. J. Electron. 27 718 [12] Zhao R, Cheng T, Wang R, Fan X and Ma H 2024 New J. Phys. 26 053016 [13] Wang H, Xue Y, Qu Y, Mu, X and Ma H 2022 npj Quantum Information 8 134 [14] Adleman L M 1994 Science 266 1021 [15] Chang W L, Guo M and Ho M S H 2005 IEEE Transactions on Nanobioscience 4 149 [16] Ning K 2012 Computers & Electrical Engineering 38 1240 [17] Huang X and Ye G 2018 Multimedia Tools and Applications 72 57 [18] Ur Rehman A, Liao X, Ashraf R, Ullah S, andWang H 2018 Optik 159 348 [19] Imre S 2014 Computers & Electrical Engineering 40 134 [20] Gupta M,and Nene M J 2020 2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI) pp. 1 [21] Abura’ed N, Khan F S and Bhaskar H2017 ACM Computing Surveys (CSUR) 49 1 [22] Farhi E and Neven H 2021 International Journal of Theoretical Physics 60 2930 [23] Hu W W, Zhou R G, Luo J, Jiang S X and Luo G F 2021 Quantum Inf. Process. 19 1 [24] Liu X, Xiao D, Huang W and Liu C 2019 IEEE Access 7 57188 [25] Guo L, Du H and Huang D 2022 Quantum Inf. Process. 21 20 [26] Le P Q, Dong F and Hirota K 2011 Quantum Inf. Process. 10 63 [27] Zhang Y, Lu K, Gao Y and Wang M 2013 Quantum Inf. Process. 12 2833 [28] Zhang Y, Lu K, Gao Y and Xu K 2013 Quantum Inf. Process. 12 3103 [29] Sun B, Iliyasu A, Yan F, Dong F and Hirota K 2021 J. Adv. Comput. Intell. Intell. Inform 17 3 [30] Li H S, Zhu Q, Zhou R G, Song L and Yang X J 2014 Quantum Inf. Process. 13 991 [31] Hao W, Zhang T, Chen X and Zhou X 2023 Signal Processing 105 108890 [32] Liu X 2023 Physica Scripta 98 115112 [33] Zhu H H, Chen X B and Yang Y X 2021 Quantum Inf. Process. 20 315 [34] Li H S, Li C, Chen X and Xia H Y 2018 International Journal of Theoretical Physics 57 3745 [35] Jiang N, Wu W Y and Wang L 2014 Quantum Inf. Process. 13 1223 [36] Jiang N, Wang L and Wu W Y 2014 International Journal of Theoretical Physics 53 2463 [37] Jin C and Liu H 2017 Int. J. Netw. Secur 19 347 [38] Zhao J, Zhang T, Jiang J, Fang T and Ma H 2022 Scientific Reports 12 14253 [39] Zhu H, Chen Z and Leng T 2024 J. Appl. Phys. 135 014401 [40] Wei X, Guo L, Zhang Q, Zhang J and Lian S2012 Journal of Systems and Software 85 290 [41] Sharma N, Jain V and Mishra A 2018 Journal of Systems and Software 132 377 [42] Gao J, Wang Y, Song Z and Wang S 2023 Entropy 25 865 [43] Gong L H, He X T, Cheng S, Hua T X and Zhou N R 2016 International Journal of Theoretical Physics55 3234 [44] Rajakumaran C and Kavitha R 2020 Multimedia Tools and Applications 79 23849 [45] Abd El-Latif A A, Abd-El-Atty B and Talha M 2017 IEEE Access 6 1073 [46] Abanda Y and Tiedeu A 2016 IET Image Processing 10 742 [47] Li C and Yang X 2022 Optik 260 169042 [48] Kumari M, Gupta S and Sardana P 2017 3D Research 8 37 |
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