Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

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

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 to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. 1.

    https://www.facebook.com.

  2. 2.

    http://gmplib.org/.

  3. 3.

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

References

  1. 1.

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

  2. 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)

  3. 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)

  4. 4.

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

  5. 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)

  6. 6.

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

  7. 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)

  8. 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

  9. 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)

  10. 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

  11. 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)

  12. 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)

  13. 13.

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

  14. 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. 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)

  16. 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)

  17. 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)

  18. 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)

  19. 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. 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

  21. 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

  22. 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)

  23. 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)

  24. 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)

  25. 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)

  26. 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)

  27. 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)

  28. 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)

  29. 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)

  30. 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. 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. 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. 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)

  34. 34.

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

  35. 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)

  36. 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)

  37. 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. 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. 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)

  40. 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)

  41. 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. 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)

  43. 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. 44.

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

  45. 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)

  46. 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)

  47. 47.

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

  48. 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. 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)

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

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

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

Keywords

  • Image protection
  • Cloud computing
  • Near-duplicate detection
  • Deep learning