Skip to main content

Advertisement

Springer Nature Link
Log in

Secure real-time image protection scheme with near-duplicate detection in cloud computing

  • Special Issue Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

With advancements in technologies of the Internet and multi-media, various images need to be generated and transmitted anytime. Restricted by local constrained storage space, users can store their images with the assist of the cloud. However, the cloud is a remote semi-trusted party that may extract stored images for adversaries due to monetary reasons. In this paper, a secure real-time image protection scheme is proposed, which can be used to enhance the security of the stored images in cloud computing. Moreover, the convergent encryption is used to construct our scheme, which can provide functionalities of image deduplication checking and near-duplicate detection for the image owner. To improve the efficiency of the near-duplicate detection, deep learning is exploited in our scheme to extract images. Security analysis indicates that the proposed scheme can meet the security requirements of correctness and security. Performance analysis shows that the proposed scheme can be performed with low computational cost.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Notes

  1. https://www.facebook.com.

  2. http://gmplib.org/.

  3. https://crypto.stanford.edu/pbc/.

References

  1. Gao, Q., Zhang, L., Zhang, D., Xu, H.: Independent components extraction from image matrix. Pattern Recogn. Lett. 31(3), 171–178 (2010)

    Article  Google Scholar 

  2. Wang, H., Ahuja, N.: Rank-r approximation of tensors: using image-as-matrix representation. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2, 346–353 (2005)

    Google Scholar 

  3. Chang, L., Yu, C., Yan, L., Chen, G., Vokkarane, V., Ma, Y.: Deepfood: deep learning-based food image recognition for computer-aided dietary assessment. Int. Conf. Smart Homes Health Telemat. 9677, 37–48 (2016)

    Google Scholar 

  4. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  5. Razzak, M.I., Naz, S., Zaib, A.: Deep learning for medical image processing: Overview, challenges and the future. In: Classification in BioApps, pp. 323–350 (2018)

    Google Scholar 

  6. Mell, P., Grance, T.: The nist definition of cloud computing. Commun. ACM 53(6), 50 (2011)

    Google Scholar 

  7. Shen, J., Liu, D., Lai, C.F., Ren, Y., Wang, J., Sun, X.: A secure identity-based dynamic group data sharing scheme for cloud computing. J. Internet Technol. 18(4), 833–842 (2017)

    Google Scholar 

  8. Zhou, T., Shen, J., Li, X., Wang, C., Shen, J.: Quantum cryptography for the future internet and the security analysis. Secur. Commun. Netw. (2018). https://doi.org/10.1155/2018/8214619

    Article  Google Scholar 

  9. Chen, X., Huang, X., Jin, L., Ma, J., Lou, W., Wong, D.S.: New algorithms for secure outsourcing of largescale systems of linear equations. IEEE Trans. Inf. Forensics Secur. 10(1), 69–78 (2014)

    Article  Google Scholar 

  10. Zhang, M., Zhang, Y., Jiang, Y., Shen, J.: Obfuscating EVES algorithm and its application in fair electronic transactions in public cloud systems. IEEE Syst. J. (2019). https://doi.org/10.1109/jsyst.2019.2900723

    Article  Google Scholar 

  11. Chen, X., Li, J., Ma, J., Qiang, T., Lou, W.: New algorithms for secure outsourcing of modular exponentiations. IEEE Trans. Parallel Distrib. Syst. 25(9), 2386–2396 (2012)

    Article  Google Scholar 

  12. Zhang, Y., Yu, R., Yao, W., Xie, S., Miao, Y., Guizani, M.: Home m2m networks: architectures, standards, and qos improvement. IEEE Commun. Mag. 49(4), 44–52 (2011)

    Article  Google Scholar 

  13. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., Ghalsasi, A.: Cloud computing-the business perspective. Decis. Support Syst. 51(1), 176–189 (2011)

    Article  Google Scholar 

  14. Wang, C., Shen, J., Lai, C.F., Huang, R., Wei, F.: Neighborhood trustworthiness based vehicle-to-vehicle authentication scheme for vehicular ad hoc networks. Concurr. Comput. Pract. Exp. (2018). https://doi.org/10.1002/cpe.4643

  15. Chen, X., Li, J., Huang, X., Ma, J., Lou, W.: New publicly verifiable databases with efficient updates. IEEE Trans. Dependable Secure Comput. 12(5), 546–556 (2015)

    Article  Google Scholar 

  16. Maharjan, S., Zhu, Q., Zhang, Y., Gjessing, S., Basar, T.: Dependable demand response management in the smart grid: a stackelberg game approach. IEEE Trans. Smart Grid 4(1), 120–132 (2013)

    Article  Google Scholar 

  17. Chen, X., Li, J., Huang, X., Li, J., Xiang, Y., Wong, D.S.: Secure outsourced attribute-based signatures. IEEE Trans. Parallel Distrib. Syst. 25(12), 3285–3294 (2014)

    Article  Google Scholar 

  18. Liu, D., Shen, J., Wang, A., Wang, C.: Lightweight and practical node clustering authentication protocol for hierarchical wireless sensor networks. Int. J. Sens. Netw. 27(2), 95–102 (2018)

    Article  Google Scholar 

  19. Shen, J., Liu, D., Bhuiyan, M.Z.A., Shen, J., Sun, X., Castiglione, A.: Secure verifiable database supporting efficient dynamic operations in cloud computing. In: IEEE Transactions on Emerging Topics in Computing (2017). https://doi.org/10.1109/tetc.2017.2776402

  20. Zhou, T., Shen, J., Li, X., Wang, C., Tan, H.: Logarithmic encryption scheme for cyber-physical systems employing fibonacci q-matrix. Future Gener Comput Syst (2018). https://doi.org/10.1016/j.future.2018.04.008

    Article  Google Scholar 

  21. Sun, N., Zhang, J., Rimba, P., Gao, S., Xiang, Y.: Data-driven cybersecurity incident prediction and discovery: a survey. IEEE Commun Surv Tutor (2018). https://doi.org/10.1109/comst.2018.2885561

    Article  Google Scholar 

  22. Liu, L., Vel, D.O., Han, Q.-L., Zhang, J., Xiang, Y.: Detecting and preventing cyber insider threats: a survey. IEEE Commun. Surv. Tutor. 20(2), 1397–1417 (2018)

    Article  Google Scholar 

  23. Jiang, J., Wen, S., Yu, S., Xiang, Y., Zhou, W.: Identifying propagation sources in networks: state-of-the-art and comparative studies. IEEE Commun. Surv. Tutor. 19(1), 465–481 (2017)

    Article  Google Scholar 

  24. Dikaiakos, M.D., Katsaros, D., Mehra, P., Pallis, G., Vakali, A.: Cloud computing: distributed internet computing for it and scientific research. IEEE Internet Comput. 13(5), 10–13 (2009)

    Article  Google Scholar 

  25. Wu, T., Wen, S., Xiang, Y., Zhou, W.: Twitter spam detection: survey of new approaches and comparative study. Comput. Secur. 76, 265–284 (2018)

    Article  Google Scholar 

  26. Zhang, J., Xiang, Y., Wang, Y., Zhou, W., Xiang, Y., Guan, Y.: Network traffic classification using correlation information. IEEE Trans. Parallel Distrib. Syst. 24(1), 104–117 (2013)

    Article  Google Scholar 

  27. Wen, S., Haghighi, M.S., Chen, C., Xiang, Y., Zhou, W., Jia, W.: A sword with two edges: propagation studies on both positive and negative information in online social networks. IEEE Trans. Comput. 64(3), 640–653 (2015)

    Article  MathSciNet  Google Scholar 

  28. Zhang, Y., Yu, R., Nekovee, M., Liu, Y., Xie, S., Gjessing, S.: Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE Netw. Mag. 26(3), 6–13 (2012)

    Article  Google Scholar 

  29. Chong, F., Meng, W., Zhan, Y., Zhu, Z., Lau, F.C.M., Chi, K.T., Ma, H.: An efficient and secure medical image protection scheme based on chaotic maps. Comput. Biol. Med. 43(8), 1000–1010 (2013)

    Article  Google Scholar 

  30. Hou, Y.C., Huang, P.H.: Image protection based on visual cryptography and statistical property. In: IEEE Statistical Signal Processing Workshop, pp. 481–484 (2011)

  31. Isa, M.R.M., Aljareh, S.: Biometric image protection based on discrete cosine transform watermarking technique. In: International Conference on Engineering & Technology, pp. 1–5 (2012)

  32. Masmoudi, A., Puech, W., Bouhlel, M.S.: A generalized continued fraction-based asynchronous stream cipher for image protection. In: European Signal Processing Conference, pp. 24–28 (2009)

  33. Sarreshtedari, S., Ali, A.: A source-channel coding approach to digital image protection and self-recovery. IEEE Trans. Image Process. 24(7), 2266–2277 (2015)

    Article  MathSciNet  Google Scholar 

  34. Hsia, C.-H.: New verification method for finger-vein recognition system. IEEE Sens. J. 18(2), 790–797 (2018)

    Article  Google Scholar 

  35. Chen, S.-L., Nie, J., Lin, T.-L., Chung, R.-L., Hsia, C.-H., Liu, Z.-Y., Wu, H.-X.: VLSI implementation of an ultra low-cost and low-power image compressor for wireless camera networks. J. Real Time Image Proc. 14(4), 803–812 (2018)

    Article  Google Scholar 

  36. Hsia, C.-H., Wu, T.-C., Chiang, J.-S.: A new method of moving object detection using adaptive filter. J. Real Time Image Proc. 13(2), 311–325 (2017)

    Article  Google Scholar 

  37. Bueno, L.M., Valle, E., Torres, R.: Bayesian approach for near-duplicate image detection. In: ACM International Conference on Multimedia Retrieval, pp. 15–18 (2012)

  38. Hu, Y., Li, M., Yu, N.: Efficient near-duplicate image detection by learning from examples. In: IEEE International Conference on Multimedia and Expo, pp. 657–660 (2008)

  39. Kim, H.s., Chang, H.W., Lee, J., Lee, D.: Basil: effective near-duplicate image detection using gene sequence alignment. In: European Conference on Information Retrieval, pp. 229–240 (2010)

    Google Scholar 

  40. Xie, H., Gao, K., Zhang, Y., Tang, S., Li, J., Liu, Y.: Efficient feature detection and effective post-verification for large scale near-duplicate image search. IEEE Trans. Multimed. 13(6), 1319–1332 (2011)

    Article  Google Scholar 

  41. Zheng, L., Qiu, G., Huang, J., Fu, H.: Salient covariance for near-duplicate image and video detection. In: IEEE International Conference on Image Processing, pp. 2537–2540 (2011)

  42. Bellare, M., Keelveedhi, S., Ristenpart, T.: Messagelocked encryption and secure deduplication. In: International Conference on the Theory and Applications of Cryptographic Techniques, pp. 296–312 (2013)

    Chapter  Google Scholar 

  43. Douceur, J.R., Adya, A., Bolosky, W.J., Simon, P., Theimer, M.: Reclaiming space from duplicate files in a serverless distributed file system. In: International Conference on Distributed Computing Systems, pp. 617–624 (2002)

  44. Li, J., Li, J., Xie, D., Zhang, C.: Secure auditing and deduplicating data in cloud. IEEE Trans. Comput. 65(8), 2386–2396 (2016)

    Article  MathSciNet  Google Scholar 

  45. Hsu, C.Y., Lu, C.S., Pei, S.C.: Image feature extraction in encrypted domain with privacy-preserving sift. IEEE Trans. Image Process. 21(11), 4593–4607 (2012)

    Article  MathSciNet  Google Scholar 

  46. Nian, F., Li, T., Wu, X., Gao, Q., Li, F.: Efficient nearduplicate image detection with a local-based binary representation. Multimed. Tools Appl. 75(5), 2435–2452 (2016)

    Article  Google Scholar 

  47. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Signal Process. 7(3), 197–387 (2014)

    Article  MathSciNet  Google Scholar 

  48. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, pp. 770–778 (2016)

  49. Rogaway, P., Shrimpton, T.: Cryptographic hash-function basics: definitions, implications, and separations for preimage resistance, second-preimage resistance, and collision resistance. In: International Workshop on Fast Software Encryption, pp. 371–388 (2004)

    Chapter  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant nos. U1836115, 61672295, and 61672290, the Natural Science Foundation of Jiangsu Province under Grant no. BK20181408, the Foundation of State Key Laboratory of Cryptology under Grant no. MMKFKT201830, the Foundation of Guizhou Provincial Key Laboratory of Public Big Data under Grant no. 2018BDKFJJ003, the CICAEET fund, and the PAPD fund. This work is also supported in part by MOST under contracts 108-2634-F-259-001 through Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan.

Author information

Authors and Affiliations

  1. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Dengzhi Liu, Jian Shen, Anxi Wang & Chen Wang

  2. State Key Laboratory of Cryptology, Beijing, 100878, China

    Jian Shen

  3. Guizhou Provincial Key Laboratory of Public Big Data, Guiyang, 550025, China

    Jian Shen

Authors
  1. Dengzhi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anxi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian Shen.

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

Liu, D., Shen, J., Wang, A. et al. Secure real-time image protection scheme with near-duplicate detection in cloud computing. J Real-Time Image Proc 17, 175–184 (2020). https://doi.org/10.1007/s11554-019-00887-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-019-00887-6

Keywords

Search

Navigation