TPE-ISE: approximate thumbnail preserving encryption based on multilevel DWT information self-embedding

Springer Science and Business Media LLC - Tập 53 - Trang 4027-4046 - 2022
Yinjing Wang1, Xiuli Chai1, Zhihua Gan2, Yushu Zhang3, Xiuhui Chen1, Xin He2
1School of Artificial Intelligence, Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
2School of Software, Intelligent Data Processing Engineering Research Center of Henan Province, Institute of Intelligent Network System, Henan University, Kaifeng, China
3College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Tóm tắt

With the development of cloud storage, more and more users upload images to the cloud. However, images stored in the cloud face the risk of unauthorized data mining by cloud service providers and being stolen by hackers. Encryption can protect image privacy, but traditional image encryption algorithms sacrifice image usability for security. To protect privacy while preserving image usability, two approximate thumbnail-preserving encryption (TPE) schemes, called dynamic range preserving encryption (DRPE) and approximate TPE with LSB Embedding (TPE-LSB), have been presented by Marohn in 2017. However, there is the possibility of decryption failure for DRPE, the cipher image robustness is poor for TPE-LSB, which cannot resist noise attacks. Additionally, both methods have the problems of the high file expansion rate of cipher image and poor perceptual quality of cipher image thumbnail. To solve these problems, a multi-level DWT information self-embedding method for thumbnail preserving encryption (TPE-ISE) is proposed. Compared with the previous works, the TPE-ISE scheme achieves a controllable compression ratio of cipher images, the perceptual quality of cipher images is closer to that of plain images, and the ability of cipher images to resist noise attacks is stronger. A series of experiments verify the superiority of the proposed algorithm.

Tài liệu tham khảo

Carrington D (2020) How many photos will be taken in 2020? Mylio.https://focus.mylio.com/tech-today/how-many-photos-will-be-taken-in-2020. Accessed 25 Jun 2020 Schiffer Z (2019) The big Facebook outage offers a behind-the-scenes look at how the social network’s AI “sees” your photos and interprets them for blind users, INSIDER Torbet G (2019) Pegasus spyware can break into users’ cloud accounts and steal data, Digit. Trends. https://www.digitaltrends.com/web/pegasus-nso-cloud/. Accessed 21 Jul 2019 Mazurek ML, Liang Y, Melicher W, Sleeper M, Bauer L, Ganger GR, Gupta N, Reiter MK (2014) Toward strong, usable access control for shared distributed data, in: Proc. 12th USENIX Conf. File Storage Technol. FAST 2014, pp 89–103 Yuan L, Theytaz J, Ebrahimi T (2017) Context-dependent privacy-aware photo sharing based on machine learning. In: IFIP Int. Conf. ICT Syst Secur Priv Prot, 93–107. https://doi.org/10.1007/978-3-319-58469-0_7 Tierney M, Spiro I, Bregler C, Subramanian L (2013) Cryptagram: Photo privacy for online social media. In: Proc. First ACM Conf. Online Soc. Networks, pp 75–87. https://doi.org/10.1145/2512938.2512939 Hua Z, Zhu Z, Yi S, Zhang Z, Huang H (2021) Cross-plane colour image encryption using a two-dimensional logistic tent modular map. Inf Sci 546:1063–1083. https://doi.org/10.1016/j.ins.2020.09.032 hua Gan Z, li Chai X, Han D, Chen Y (2019) A chaotic image encryption algorithm based on 3-D bit-plane permutation. Neural Comput Appl 31:7111–7130. https://doi.org/10.1007/s00521-018-3541-y Khan M, Alanazi AS, Khan LS, Hussain I (2021) An efficient image encryption scheme based on fractal Tromino and Chebyshev polynomial. Compl Intell Syst 7:2751–2764. https://doi.org/10.1007/s40747-021-00460-4 Uddin M, Jahan F, Islam MK, Hassan MR (2021) A novel DNA-based key scrambling technique for image encryption. Complex Intell Syst 7(6):3241–3258. https://doi.org/10.1007/s40747-021-00515-6 Zhang Q, Wang G, Liu Q (2019) Enabling cooperative privacy-preserving personalized search in cloud environments. Inf Sci 480:1–13. https://doi.org/10.1016/j.ins.2018.12.016 Song DX, Wagner D, Perrig A (2000) Practical techniques for searches on encrypted data. In: Proc IEEE Symp Secur Priv, 2000, pp 44–55. https://doi.org/10.1109/secpri.2000.848445 Goh EJ (2003) Secure indexes. In: Proc IACR Cryptol ePrint Arch, 2003, pp 216. http://eprint.iacr.org/2003/216 Tian Y, Hou Y, Yuan J (2017) CAPIA: Cloud assisted privacy-preserving image annotation. In: IEEE Conf Commun Netw Secur, 2017, pp 1–9. https://doi.org/10.1109/CNS.2017.8228627 Ke X, Zou J, Niu Y (2019) End-to-end automatic image annotation based on deep CNN and multi-label data augmentation. IEEE Trans Multimed 21:2093–2106. https://doi.org/10.1109/TMM.2019.2895511 Lu W, Swaminathan A, Varna AL, Wu M (2009) Enabling search over encrypted multimedia databases. Media Forensics Secur 725418. https://doi.org/10.1117/12.806980 Shen M, Cheng G, Zhu L, Du X, Hu J (2020) Content-based multi-source encrypted image retrieval in clouds with privacy preservation. Futur Gener Comput Syst 109:621–632 Majhi M, Pal AK, Islam SH, Khurram M, Khan (2021) Secure content-based image retrieval using modified Euclidean distance for encrypted features. Trans Emerg Telecommun Technol 32:e4013. https://doi.org/10.1002/ett.4013 Gregory RL, Gregory L (1997) Knowledge in perception and illusion. Philos Trans Biol Sci 352:1121–1127 Denning T, Bowers K, Van Dijk M, Juels A (2011) Exploring implicit memory for painless password recovery. In: Proc. SIGCHI Conf. Hum. Factors Comput. Syst, pp 2615–2618. https://doi.org/10.1145/1978942.1979323 Yuan L, Ebrahimi T (2017) Image privacy protection with secure JPEG transmorphing. IET Signal Process 11:1031–1038. https://doi.org/10.1049/iet-spr.2016.0756 Von Zezschwitz E, Ebbinghaus S, Hussmann H, De Luca A (2016) You can’t watch this! Privacy-respectful photo browsing on smartphones. In: Proc 2016 CHI Conf Hum Factors Comput Syst, pp 4320–4324. https://doi.org/10.1145/2858036.2858120 Padilla-López JR, Chaaraoui AA, Flórez-Revuelta F (2015) Visual privacy protection methods: A survey. Expert Syst Appl 42:4177–4195. https://doi.org/10.1016/j.eswa.2015.01.041 Sun J, Liao X, Chen X, Guo S (2017) Privacy-aware image encryption based on logstic map and data hiding. Int J Bifurc Chaos 27:1750073. https://doi.org/10.1142/S0218127417500730 wen Xue H, Du J, liang Li S, Ma W (2018) Region of interest encryption for color images based on a hyperchaotic system with three positive Lyapunov exponets. Opt Laser Technol 106:506–516. https://doi.org/10.1016/j.optlastec.2018.04.030 Wright CV, Feng WC, Liu F (2015) Thumbnail preserving encryption for JPEG. In: Proc. 3rd ACM Work. Inf. Hiding Multimed. Secur, pp 141–146. https://doi.org/10.1145/2756601.2756618 Marohn B, Wright CV, Feng WC, Rosulek M, Bobba RB (2017) Approximate thumbnail preserving encryption. In: Proc. 2017 Multimed. Priv. Secur, pp 33–43. https://doi.org/10.1145/3137616.3137621 Zhang Y, Zhao R, Xiao X, Lan R, Liu Z, Zhang X (2022) High-fidelity thumbnail-preserving encryption. IEEE Trans Circ Syst Video Technol 32:947–961. https://doi.org/10.1109/TCSVT.2021.3070348 Tajik K, Gunasekaran A, Dutta R, Ellis B, Bobba RB, Rosulek M, Wright CV, Feng W (2019) Balancing image privacy and usability with thumbnail-preserving encryption. In: Proc. Symp. Netw. Distrib. Syst. Secur. https://doi.org/10.14722/ndss.2019.23432 Zhao R, Zhang Y, Xiao X, Ye X, Lan R (2021) TPE2: Three-pixel exact thumbnail-preserving image encryption. Sig Process 183:108019. https://doi.org/10.1016/j.sigpro.2021.108019 Bao L, Zhou Y (2015) Image encryption: Generating visually meaningful encrypted images. Inf Sci 324:197–207. https://doi.org/10.1016/j.ins.2015.06.049 Kanso A, Ghebleh M (2017) An algorithm for encryption of secret images into meaningful images. Opt Lasers Eng 90:196–208. https://doi.org/10.1016/j.optlaseng.2016.10.009 Chai X, Gan Z, Chen Y, Zhang Y (2017) A visually secure image encryption scheme based on compressive sensing. Signal Process 134:35–51. https://doi.org/10.1016/j.sigpro.2016.11.016 Wang H, Xiao D, Li M, Xiang Y, Li X (2019) A visually secure image encryption scheme based on parallel compressive sensing. Signal Process 155:218–232. https://doi.org/10.1016/j.sigpro.2018.10.001 Chai X, Wu H, Gan Z, Han D, Zhang Y, Chen Y (2021) An efficient approach for encrypting double color images into a visually meaningful cipher image using 2D compressive sensing. Inf Sci 556:305–340. https://doi.org/10.1016/j.ins.2020.10.007 Wen W, Hong Y, Fang Y, Li M, Li M (2020) A visually secure image encryption scheme based on semi-tensor product compressed sensing. Signal Process 173:107580. https://doi.org/10.1016/j.sigpro.2020.107580 Wen W, Zhang Y, Fang Y, Fang Z (2018) Image salient regions encryption for generating visually meaningful ciphertext image. Neural Comput Appl 29:653–663. https://doi.org/10.1007/s00521-016-2490-6 Li XW, Cho SJ, Kim ST (2014) High security and robust optical image encryption approach based on computer-generated integral imaging pickup and iterative back-projection techniques. Opt Lasers Eng 55:162–182. https://doi.org/10.1016/j.optlaseng.2013.10.024 Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Work., pp 1132–1140. https://doi.org/10.1109/CVPRW.2017.151 Wang L, Wang Y, Dong X, Xu Q, Yang J, An W, Guo Y (2021) Unsupervised degradation representation learning for blind super-resolution. In: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp 10581–10590. http://arxiv.org/abs/2104.00416 Zhu M, Han K, Zhang C, Lin J, Wang Y (2019) Low-resolution visual recognition via deep feature distillation. IEEE Int Conf Acoust Speech Signal Process, 3762–3766. https://doi.org/10.1109/ICASSP.2019.8682926 Le V, Brandt J, Lin Z, Bourdev L, Huang TS (2012) Interactive facial feature localization. Eur Conf Comput Vis, 679–692. https://doi.org/10.1007/978-3-642-33712-3_49 Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometry consistency for large scale image search extended version. In: Proc. 10th Eur. Conf. Comput. Vis. (ECCV ’08) Wang Z, Simoncelli EP, Bovik AC (2003) Multi-scale structural similarity for image quality assessment, in: Thrity-Seventh Asilomar Conf. Signals Syst Comput 1398–1402. https://doi.org/10.1109/acssc.2003.1292216 Chai X, Zhi X, Gan Z, Zhang Y, Chen Y, Fu J (2021) Combining improved genetic algorithm and matrix semi-tensor product (STP) in color image encryption. Signal Process 183:108041. https://doi.org/10.1016/j.sigpro.2021.108041 Shi M, Guo S, Song X, Zhou Y, Wang E (2021) Visual secure image encryption scheme based on compressed sensing and regional energy. Entropy 23:570. https://doi.org/10.3390/e23050570 Ye G, Pan C, Dong Y, Shi Y, Huang X (2020) Image encryption and hiding algorithm based on compressive sensing and random numbers insertion. Signal Process 172:107563. https://doi.org/10.1016/j.sigpro.2020.107563 Zhu L, Song H, Zhang X, Yan M, Zhang T, Wang X, Xu J (2020) A robust meaningful image encryption scheme based on block compressive sensing and SVD embedding. Signal Process 175:107629. https://doi.org/10.1016/j.sigpro.2020.107629 Wang X, Liu C, Jiang D (2021) A novel triple-image encryption and hiding algorithm based on chaos, compressive sensing and 3D DCT. Inf Sci 574:505–527. https://doi.org/10.1016/j.ins.2021.06.032 Hua Z, Zhang K, Li Y, Zhou Y (2021) Visually secure image encryption using adaptive-thresholding sparsification and parallel compressive sensing. Signal Process 183:107998. https://doi.org/10.1016/j.sigpro.2021.107998 Chai X, Wu H, Gan Z, Zhang Y, Chen Y, Nixon KW (2020) An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding. Opt Lasers Eng 124:105837. https://doi.org/10.1016/j.optlaseng.2019.105837