Skip to main content
SpringerLink
Log in

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

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

    Article  Google Scholar 

  8. 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

  9. 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

    Article  Google Scholar 

  10. 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

  11. 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

    Article  Google Scholar 

  12. 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

  13. Goh EJ (2003) Secure indexes. In: Proc IACR Cryptol ePrint Arch, 2003, pp 216. http://eprint.iacr.org/2003/216

  14. 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

  15. 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

    Article  Google Scholar 

  16. 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

  17. 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

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. Gregory RL, Gregory L (1997) Knowledge in perception and illusion. Philos Trans Biol Sci 352:1121–1127

    Article  Google Scholar 

  20. 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

  21. 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

    Article  Google Scholar 

  22. 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

  23. 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

    Article  Google Scholar 

  24. 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

  25. 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

    Article  Google Scholar 

  26. 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

  27. 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

  28. 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

    Article  Google Scholar 

  29. 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

  30. 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

    Article  Google Scholar 

  31. 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

    Article  MATH  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. 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

    Article  Google Scholar 

  35. 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

    Article  MATH  Google Scholar 

  36. 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

    Article  Google Scholar 

  37. 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

    Article  Google Scholar 

  38. 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

    Article  Google Scholar 

  39. 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

  40. 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

  41. 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

  42. 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

  43. 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)

  44. 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

  45. 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

    Article  Google Scholar 

  46. 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

    Article  Google Scholar 

  47. 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

    Article  Google Scholar 

  48. 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

    Article  Google Scholar 

  49. 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

    Article  Google Scholar 

  50. 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

    Article  Google Scholar 

  51. 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

    Article  Google Scholar 

Download references

Acknowledgements

All the authors are deeply grateful to the editors for smooth and fast handling of the manuscript. The authors would also like to thank the anonymous referees for their valuable suggestions to improve the quality of this paper. This work is supported by the National Natural Science Foundation of China (Grant Nos. 61802111, 61872125, 62171114), and the Key Science and Technology Project of Henan Province (Grant Nos. 201300210400, 212102210094), and Guangxi Key Laboratory of Trusted Software (Grant no. KX202027).

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, China

    Yinjing Wang, Xiuli Chai & Xiuhui Chen

  2. School of Software, Intelligent Data Processing Engineering Research Center of Henan Province, Institute of Intelligent Network System, Henan University, 475004, Kaifeng, China

    Zhihua Gan & Xin He

  3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China

    Yushu Zhang

Authors
  1. Yinjing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiuli Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhihua Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yushu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiuhui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xin He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhihua Gan or Xin He.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Chai, X., Gan, Z. et al. TPE-ISE: approximate thumbnail preserving encryption based on multilevel DWT information self-embedding. Appl Intell 53, 4027–4046 (2023). https://doi.org/10.1007/s10489-022-03597-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03597-y

Keywords

Search

Navigation