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A privacy-preserving and traitor tracking content-based image retrieval scheme in cloud computing

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Abstract

To ensure the image security, a large number of ciphertext image retrieval methods have been studied and applied, such as homomorphic encryption and multi-key encryption. However, most of these algorithms do not consider the protection of image copyright, user information and traitor tracking. For this reason, this paper proposes a privacy-preserving and traitor tracking content-based image retrieval scheme in cloud computing. This method introduces the DenseNet network to strengthen statistical features. One-way hash algorithm and XOR operation are used to protect copyright and user information, and reversible information hiding algorithm is accessed for traitor tracking. Experimental results show that compared with other algorithms that support leak tracking, our method achieves higher retrieval precision, higher retrieval efficiency and simpler architecture.

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References

  1. Cheng, H., Zhang, X., Yu, J.: Ac-coefficient histogram-based retrieval for encrypted jpeg images. Multimed. Tools Appl. 75(21), 13791–13803 (2016)

    Article  Google Scholar 

  2. Coltuc, D.: Improved embedding for prediction-based reversible watermarking. IEEE Trans. Inf. Forensics Secur. 6(3), 873–882 (2011)

    Article  Google Scholar 

  3. Ferreira, B., Rodrigues, J., Leitao, J., Domingos, H.: Practical privacy-preserving content-based retrieval in cloud image repositories. IEEE Trans. Cloud Comput. 7(3), 784–798 (2017)

    Article  Google Scholar 

  4. Li, X., Zhang, W., Gui, X., Yang, B.: Efficient reversible data hiding based on multiple histograms modification. IEEE Trans. Inf. Forensics Secur. 10(9), 2016–2027 (2017)

    Google Scholar 

  5. Luo, Y., Qin, J., Xiang, X., Tan, Y.: Coverless image steganography based on multi-object recognition. IEEE Trans. Circuits Syst. Video Technol (2020). https://doi.org/10.1109/TCSVT.2020.3033945

    Article  Google Scholar 

  6. Liang, Haihua, Zhang, Xinpeng, Wei, Qiuhan, Cheng, Hang: Secure image retrieval with multiple keys. J. Electron. Imaging 27(2) ,023032 (2018)

    Article  Google Scholar 

  7. Liu, Q., Xiang, X., Qin, J., Tan, Y., Tan, J., Luo, Y.: Coverless steganography based on image retrieval of densenet features and dwt sequence mapping. Knowl. Based Syst. 192(2020), 105375–105389 (2020)

    Article  Google Scholar 

  8. Luo, Y., Qin, J., Xiang, X., Tan, Y., Liu, Q., Xiang, L.: Coverless real-time image information hiding based on image block matching and dense convolutional network. J. Real-Time Image Process. 17(1), 125–135 (2020)

    Article  Google Scholar 

  9. Ma, W., Qin, J., Xiang, X., Tan, Y., He, Z.: Searchable encrypted image retrieval based on multi-feature adaptive late-fusion. Mathematics 8(6), 1019 (2020)

    Google Scholar 

  10. Ni, Z., Shi, Y.Q., Ansari, N., Su, W.: Reversible data hiding. IEEE Trans. Circuits Syst. Video Technol. 16(3), 354–362 (2006)

    Article  Google Scholar 

  11. Norouzi, M., Fleet, D.J.D.D.J., Salakhutdinov, R., Blei, D.M.: Hamming distance metric learning. Adv. Neural Inf. Process. Syst. 25, 1061–1069 (2012)

    Google Scholar 

  12. Qin, J., Li, H., Xiang, X., Tan, Y., Pan, W., Ma, W., Xiong, N.N.: An encrypted image retrieval method based on Harris corner optimization and lsh in cloud computing. IEEE Access 7(1), 24626–24633 (2019)

    Article  Google Scholar 

  13. Qin, J., Wang, J., Tan, Y., Huang, H., He, Z.: Coverless image steganography based on generative adversarial network. Mathematics 8(9), 1394 (2020)

    Article  Google Scholar 

  14. Sachnev, V., Kim, H.J., Nam, J., Suresh, S., Shi, Y.Q.: Reversible watermarking algorithm using sorting and prediction. IEEE Trans. Circuits Syst. Video Technol. 19(7), 989–999 (2009)

    Article  Google Scholar 

  15. Shen, M., Cheng, G., Zhu, L., Du, X., Hu, J.: Content-based multi-source encrypted image retrieval in clouds with privacy preservation. Future Gener. Comput. Syst. 109, 621–632 (2018)

    Article  Google Scholar 

  16. Tan, Y., Qin, J., Xiang, X., Ma, W., Pan, W., Xiong, N.N.: A robust watermarking scheme in ycbcr color space based on channel coding. IEEE Access 7(1), 25026–25036 (2019)

    Article  Google Scholar 

  17. Thodi, D.M., Rodriguez, J.J.: Expansion embedding techniques for reversible watermarking. IEEE Trans. Image Process. 16(3), 721–730 (2007)

    Article  MathSciNet  Google Scholar 

  18. Tian, J.: Reversible data embedding using a difference expansion. IEEE Trans. Circuits Syst. Video Technol. 13(8), 890–896 (2003)

    Article  Google Scholar 

  19. Wang, J., Chen, X., Ni, J., Mao, N., Shi, Y.: Multiple histograms based reversible data hiding: framework and realization. IEEE Trans. Circuits Syst. Video Technol. 30(8), 2313–2328 (2019)

    Article  Google Scholar 

  20. Wang, J., Qin, J., Xiang, X., Tan, Y., Pan, N.: Captcha recognition based on deep convolutional neural network. Math. Biosci. Eng. 16(5), 5851–5861 (2019)

    Article  MathSciNet  Google Scholar 

  21. Xia, Z., Wang, X., Zhang, L., Qin, Z., Sun, X., Ren, K.: A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans. Inf. Forensics Secur. 11(11), 2594–2608 (2016)

    Article  Google Scholar 

  22. Xia, Z., Jiang, L., Liu, D., Lu, L., Jeon, B.: Boew: a content-based image retrieval scheme using bag-of-encrypted-words in cloud computing. IEEE Trans. Serv. Comput (2019). https://doi.org/10.1109/TSC.2019.2927215

    Article  Google Scholar 

  23. Xu, W., Xiang, S., Sachnev, V.: A cryptograph domain image retrieval method based on Paillier homomorphic block encryption. CMC Comput. Mater. Continua 55(2), 285–295 (2018)

    Google Scholar 

  24. Ye, Y., Li, S., Liu, F., Tang, Y., Hu, W.: Edgefed: optimized federated learning based on edge computing. IEEE Access (2020). https://doi.org/10.1109/ACCESS.2020.3038287

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61772561, in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174, 19B584 and 18C0262, in part by the Key Research and Development Plan of Hunan Province under Grant 2019SK2022, in part by the Natural Science Foundation of Hunan Province under Grant 2020JJ4140 and 2020JJ4141, in part by the Degree and Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154, and in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant [2019]370-133.

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  1. College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China

    Zhangdong Wang, Jiaohua Qin, Xuyu Xiang & Yun Tan

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  1. Zhangdong Wang
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  2. Jiaohua Qin
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  3. Xuyu Xiang
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  4. Yun Tan
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Correspondence to Jiaohua Qin.

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Wang, Z., Qin, J., Xiang, X. et al. A privacy-preserving and traitor tracking content-based image retrieval scheme in cloud computing. Multimedia Systems 27, 403–415 (2021). https://doi.org/10.1007/s00530-020-00734-w

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