Abstract
With the substantial increase in image transmission, the demand for image security is increasing. Noise-like images can be obtained by conventional encryption schemes, and although the security of the images can be guaranteed, the noise-like images cannot be directly previewed and retrieved. Based on the rank-then-encipher method, some researchers have designed a three-pixel exact thumbnail preserving encryption (TPE2) scheme, which can be applied to balance the security and availability of images, but this scheme has low encryption efficiency. In this paper, we introduce an efficient exact thumbnail preserving encryption scheme. First, blocking and bit-plane decomposition operations are performed on the plaintext image. The zigzag scrambling model is used to change the bit positions in the lower four bit planes. Subsequently, an operation is devised to permute the higher four bit planes, which is an extended application of the hidden Markov model. Finally, according to the difference in bit weights in each bit plane, a bit-level weighted diffusion rule is established to generate an encrypted image and still maintain the same sum of pixels within the block. Simulation results show that the proposed scheme improves the encryption efficiency and can guarantee the availability of images while protecting their privacy.
摘要
随着图像传输技术日益发展, 人们对图像安全的需求也在大幅提升。由传统图像加密方案获得的类噪声图像虽然可以保证内容安全, 但无法直接用于预览和检索。一些学者基于排序后加密方法, 设计了一种三像素缩略图保留加密方案(TPE2), 用于平衡图像安全性和可用性, 然而该方案的加密效率较低。为此, 本文提出一种有效的精确缩略图保留加密方案。首先对明文图像进行分块和位平面置乱, 然后采用Z字形置乱模型改变最低的4个位平面中比特的位置, 随后介绍了用于改变最高的4个位平面中比特位置的操作(这是隐马尔科夫模型的一个扩展应用)。最后, 根据每个位平面中比特的权重不同, 设计了一种比特级分权扩散规则。至此生成的加密图像能保证块内像素和不变。仿真结果表明, 该方案在平衡图像隐私性和可用性的同时, 提高了加密效率。
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The data that support the findings of this study are available from the corresponding authors upon reasonable request.
References
Ashiq JA, 2015. Insider vs. Outsider Threats: Identify and Prevent. INFOSEC. https://resources.infosecinstitute.com/topic/insider-vs-outsider-threats-identify-and-prevent/ [Accessed on Aug. 14, 2022].
Baum LE, Petrie T, 1966. Statistical inference for probabilistic functions of finite state Markov chains. Ann Math Statist, 37(6):1554–1563. https://doi.org/10.1214/aoms/1177699147
Beaver D, Kumar S, Li HC, et al., 2010. Finding a needle in haystack: Facebook’s photo storage. 9th USENIX Symp on Operating Systems Design and Implementation, p.47–60.
Bellare M, Ristenpart T, Rogaway P, et al., 2009. Format-preserving encryption. Proc 16th Int Workshop on Selected Areas in Cryptography, p.295–312. https://doi.org/10.1007/978-3-642-05445-7_19
Chai XL, Fu JY, Gan ZH, et al., 2022a. An image encryption scheme based on multi-objective optimization and block compressed sensing. Nonl Dynam, 108(3):2671–2704. https://doi.org/10.1007/s11071-022-07328-3
Chai XL, Wang YJ, Gan ZH, et al., 2022b. Preserving privacy while revealing thumbnail for content-based encrypted image retrieval in the cloud. Inform Sci, 604:115–141. https://doi.org/10.1016/j.ins.2022.05.008
Chen LP, Yin H, Yuan LG, et al., 2021. Double color image encryption based on fractional order discrete improved Henon map and Rubik’s cube transform. Signal Process Image Commun, 97:116363. https://doi.org/10.1016/j.image.2021.116363
Dong C, Loy CC, Tang XO, 2016. Accelerating the superresolution convolutional neural network. Proc 14th European Conf on Computer Vision, p.391–407. https://doi.org/10.1007/978-3-319-46475-6_25
Fan LY, 2019. A demonstration of image obfuscation with provable privacy. IEEE Int Conf on Multimedia & Expo Workshops, p.608. https://doi.org/10.1109/ICMEW.2019.00112
Franzese M, Iuliano A, 2019. Hidden Markov models. Encycl Bioinform Comput Biol, 1:753–762. https://doi.org/10.1016/B978-0-12-809633-8.20488-3
Hartmann Y, Liu H, Lahrberg S, et al., 2022. Interpretable high-level features for human activity recognition. Proc 15th Int Joint Conf on Biomedical Engineering Systems and Technologies, p.40–49. https://doi.org/10.5220/0010840500003123
He ZL, He YH, Chen LY, 2010. A study on the key issues of cloud storage technology. Appl Mech Mater, 29–32:1122–1126. https://doi.org/10.4028/www.scientific.net/AMM.29-32.1122
Jegou H, Douze M, Schmid C, 2008. Hamming embedding and weak geometric consistency for large scale image search. Proc 10th European Conf on Computer Vision, p.304–317. https://doi.org/10.1007/978-3-540-88682-2_24
Jolfaei A, Wu XW, Muthukkumarasamy V, 2016. On the security of permutation-only image encryption schemes. IEEE Trans Inform Forens Secur, 11(2):235–246. https://doi.org/10.1109/TIFS.2015.2489178
Joshi AB, Kumar D, Mishra DC, et al., 2020. Colour-image encryption based on 2D discrete wavelet transform and 3D logistic chaotic map. J Mod Opt, 67(10):933–949. https://doi.org/10.1080/09500340.2020.1789233
Li SJ, Li CQ, Chen GR, et al., 2008. A general quantitative cryptanalysis of permutation-only multimedia ciphers against plaintext attacks. Signal Process Image Commun, 23(3):212–223. https://doi.org/10.1016/j.image.2008.01.003
Marohn B, Wright CV, Feng WC, et al., 2017. Approximate thumbnail preserving encryption. Proc Multimedia Privacy and Security, p.33–43. https://doi.org/10.1145/3137616.3137621
Mishra P, Bhaya C, Pal AK, et al., 2021. A novel binary operator for designing medical and natural image cryptosystems. Signal Process Image Commun, 98:116377. https://doi.org/10.1016/j.image.2021.116377
Srivastava RK, Shree R, Shukla AK, et al., 2022. A feature based classification and analysis of hidden Markov model in speech recognition. Proc Cyber Intelligence and Information Retrieval, p.365–379. https://doi.org/10.1007/978-981-16-4284-5_32
Tajik K, Gunasekaran A, Dutta R, et al., 2019. Balancing image privacy and usability with thumbnail-preserving encryption. Network and Distributed Systems Security Symp, p.24–27. https://doi.org/10.14722/ndss.2019.23432
Wang LG, Wang YQ, Dong XY, et al., 2021. Unsupervised degradation representation learning for blind superresolution. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.10576–10585. https://doi.org/10.1109/CVPR46437.2021.01044
Wright CV, Feng WC, Liu F, 2015. Thumbnail-preserving encryption for JPEG. Proc 3rd ACM Workshop on Information Hiding and Multimedia Security, p.141–146. https://doi.org/10.1145/2756601.2756618
Wu D, Gan JH, Zhou JX, et al., 2022. Fine-grained semantic ethnic costume high-resolution image colorization with conditional GAN. Int J Intell Syst, 37(5):2952–2968. https://doi.org/10.1002/int.22726
Xue TT, Liu H, 2022. Hidden Markov model and its application in human activity recognition and fall detection: a review. Proc 10th Int Conf in Communications Signal Processing and Systems, p.863–869. https://doi.org/10.1007/978-981-19-0390-8_108
Youngblood GM, Cook DJ, 2007. Data mining for hierarchical model creation. IEEE Trans Syst Man Cybern Part C Appl Rev, 37(4):561–572. https://doi.org/10.1109/TSMCC.2007.897341
Zhang YS, Zhao RY, Xiao XL, et al., 2022. HF-TPE: high-fidelity thumbnail-preserving encryption. IEEE Trans Circ Syst Video Technol, 32(3):947–961. https://doi.org/10.1109/TCSVT.2021.3070348
Zhao RY, Zhang YS, Xiao XL, et al., 2021. TPE2: three-pixel exact thumbnail-preserving image encryption. Signal Process, 183:108019. https://doi.org/10.1016/j.sigpro.2021.108019
Zhu Z, Wu C, Wang J, et al., 2020. A novel 3D vector decomposition for color-image encryption. IEEE Photon J, 12(2): 7800614. https://doi.org/10.1109/JPHOT.2020.2981494
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Xiuhui CHEN processed the data. Yakun MA validated the study. Fang ZUO supervised the study. Xiuli CHAI drafted the paper. Zhihua GAN helped organize the paper. Yushu ZHANG revised and finalized the paper.
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Xiuli CHAI, Xiuhui CHEN, Yakun MA, Fang ZUO, Zhi-hua GAN, and Yushu ZHANG declare that they have no conflict of interest.
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Project supported by the Pre-research Project of Songshan Laboratory, China (No.YYJC012022011), the Postgraduate Education Reform and Quality Improvement Project of Henan Province, China (Nos. YJS2022JD26 and SYLAL2023020), the Postgraduate Education Innovation Training Base, China (No. SYLJD2022008), the Science and Technology Project of Henan Province, China (Nos. 232102210109 and 232102210096), and the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness, China (No. HNTS2022019)
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Chai, X., Chen, X., Ma, Y. et al. TPE-H2MWD: an exact thumbnail preserving encryption scheme with hidden Markov model and weighted diffusion. Front Inform Technol Electron Eng 24, 1169–1180 (2023). https://doi.org/10.1631/FITEE.2200498
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DOI: https://doi.org/10.1631/FITEE.2200498