Journal of Cybersecurity and Information Management

Journal DOI

https://doi.org/10.54216/JCIM

Submit Your Paper

2690-6775ISSN (Online) 2769-7851ISSN (Print)
Full Length Article

Journal of Cybersecurity and Information Management

Volume 0 , Issue 2 , PP: 54-64, 2019 | Cite this article as | XML | PDF

A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval

Mohamed Elsharkawy 1 * , Ahmed N. Al Masri 2

  • 1 BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, USA - (Mohamed.elsharkawy@louisville.edu )
  • 2 Department of Computer Science, American University in the Emirates, Dubai, United Arab Emirates - (ahmed.almasri@aue.ae)
  • Doi: https://doi.org/10.54216/JCIM.000105

    Abstract

    From the last decades, a massive quantity of images gets generated and continues to rise to a maximum extent in the forthcoming data. The process of retrieving images based on a query image (QI) is a proficient method of accessing the visual properties from large datasets. Content-based image retrieval (CBIR) provides a way of effectively retrieving images from large databases. At the same time, image encryption techniques can be integrated into the CBIR model to retrieve the images securely. Therefore, this paper presents new image encryption with a deep learning-based secure CBIR model called IEDL-SCBIR. The proposed IEDL-SCBIR technique intends to encrypt the images as well as securely retrieve them. The proposed IEDL-SCBIR technique follows a two-stage process: optimal elliptic curve cryptography (ECC) based encryption and DL based image retrieval. The proposed model derives a cuckoo search optimization (CSO) with the ECC technique for the image encryption process in which the CSO algorithm is applied for optimal key generation. In addition, VGG based feature extraction with Euclidean distance-based similarity measurement is applied for the retrieval process. To validate the enhanced performance of the IEDL-SCBIR technique, a comprehensive results analysis takes place, and the obtained results demonstrate the betterment over the other methods.

    Keywords :

    Content-based image retrieval , Security, Image encryption , Deep learning , Optimal key generation

    References

    [1]      Wan, J., Wang, D., Hoi, S.C.H., Wu, P., Zhu, J., Zhang, Y. and Li, J., 2014, November. Deep learning for content-based image retrieval: A comprehensive study. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 157-166).

    [2]      Zhou, W., Li, H. and Tian, Q., 2017. Recent advance in content-based image retrieval: A literature survey. arXiv preprint arXiv:1706.06064.

    [3]      Tzelepi, M. and Tefas, A., 2018. Deep convolutional learning for content based image retrieval. Neurocomputing, 275, pp.2467-2478.

    [4]      Chung, Y.A. and Weng, W.H., 2017. Learning deep representations of medical images using siamese CNNs with application to content-based image retrieval. arXiv preprint arXiv:1711.08490.

    [5]      Piras, L. and Giacinto, G., 2017. Information fusion in content based image retrieval: A comprehensive overview. Information Fusion, 37, pp.50-60.

    [6]      Celik, C. and Bilge, H.S., 2017. Content based image retrieval with sparse representations and local feature descriptors: a comparative study. Pattern Recognition, 68, pp.1-13.

    [7]      Jin, C. and Ke, S.W., 2017. Content-based image retrieval based on shape similarity calculation. 3D Research, 8(3), pp.1-19.

    [8]      Xia, Z., Xiong, N.N., Vasilakos, A.V. and Sun, X., 2017. EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Information Sciences, 387, pp.195-204.

    [9]      Ferreira, B., Rodrigues, J., Leitao, J. and Domingos, H., 2015, September. Privacy-preserving content-based image retrieval in the cloud. In 2015 IEEE 34th symposium on reliable distributed systems (SRDS) (pp. 11-20). IEEE.

    [10]   Xia, Z., Wang, X., Zhang, L., Qin, Z., Sun, X. and Ren, K., 2016. A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE transactions on information forensics and security, 11(11), pp.2594-2608.

    [11]   Xia, Z., Zhu, Y., Sun, X., Qin, Z. and Ren, K., 2015. Towards privacy-preserving content-based image retrieval in cloud computing. IEEE Transactions on Cloud Computing, 6(1), pp.276-286.

    [12]   Nazir, A., Ashraf, R., Hamdani, T. and Ali, N., 2018, March. Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor. In 2018 international conference on computing, mathematics and engineering technologies (iCoMET) (pp. 1-6). IEEE.

    [13]   Liu, P., Guo, J.M., Wu, C.Y. and Cai, D., 2017. Fusion of deep learning and compressed domain features for content-based image retrieval. IEEE Transactions on Image Processing, 26(12), pp.5706-5717.

    [14]   Alzu'bi, A., Amira, A. and Ramzan, N., 2017. Content-based image retrieval with compact deep convolutional features. Neurocomputing, 249, pp.95-105.

    [15]   Mistry, Y., Ingole, D.T. and Ingole, M.D., 2018. Content based image retrieval using hybrid features and various distance metric. Journal of Electrical Systems and Information Technology, 5(3), pp.874-888.

    [16]   Hemalatha, S., Rajamani, V. and Parthasarathy, V., 2017. Design of Optimal Elliptic Curve Cryptography by using Partial Parallel Shifting Multiplier with Parallel Complementary. International Journal of Computer Systems Science and Engineering, 32(5).

    [17]   Elyasigomari, V., Lee, D.A., Screen, H.R. and Shaheed, M.H., 2017. Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification. Journal of biomedical informatics, 67, pp.11-20.

    [18]   Ha, I., Kim, H., Park, S. and Kim, H., 2018. Image retrieval using BIM and features from pretrained VGG network for indoor localization. Building and Environment, 140, pp.23-31.

    [19]   Alsmadi, M.K., 2018. Query-sensitive similarity measure for content-based image retrieval using meta-heuristic algorithm. Journal of King Saud University-Computer and Information Sciences, 30(3), pp.373-381.

     

    Cite This Article As :
    Elsharkawy, Mohamed. , N., Ahmed. A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval. Journal of Journal of Cybersecurity and Information Management, vol. 0, no. 2, 2019, pp. 54-64. DOI: https://doi.org/10.54216/JCIM.000105
    Elsharkawy, M. N., A. (2019). A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval. Journal of Journal of Cybersecurity and Information Management, 0( 2), 54-64. DOI: https://doi.org/10.54216/JCIM.000105
    Elsharkawy, Mohamed. N., Ahmed. A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval. Journal of Journal of Cybersecurity and Information Management 0, no. 2 (2019): 54-64. DOI: https://doi.org/10.54216/JCIM.000105
    Elsharkawy, M. , N., A. (2019) . A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval. Journal of Journal of Cybersecurity and Information Management , 0( 2) , 54-64 . DOI: https://doi.org/10.54216/JCIM.000105
    Elsharkawy M. , N. A. [2019]. A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval. Journal of Journal of Cybersecurity and Information Management. 0( 2): 54-64. DOI: https://doi.org/10.54216/JCIM.000105
    [1] Elsharkawy, M. [2] N., A. "A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval," Journal of Journal of Cybersecurity and Information Management, vol. 0, no. 2, pp. 54-64, 2019. DOI: https://doi.org/10.54216/JCIM.000105