EVALUATING THE DIVERSE EFFECTS OF SIGNATURE ENCRYPTION ON MVS SYSTEM PERFORMANCE
DOI:
https://doi.org/10.30572/2018/KJE/160415Keywords:
MVS, Signature encryption, Local database, Network load, MatchingAbstract
Recently, image content based information retrieval has been widely used in many applications. A Mobile Visual Search (MVS) system involves capturing an image for the object to be searched for, extracting useful features, constructing object signature and searching local and/or remote databases for a match. This paper investigates the diverse effects of signature encryption on the accuracy, search burden and response time of an MVS system. The aim of this research is to provide a comprehensive understanding of the implications of signature encryption to figure out the design trade-offs that facilitate better MVS performance under given operation hypotheses. An MVS system employing different signature encryption algorithms is simulated with different system parameters. The obtained results show that signature encryption generally adds to system processing time from 5.9 µs to about 22.7 µs in test cases 4 and 6. Moreover, it makes the MVS system more sensitive to imaging and communication link imperfections leading to less capability to correctly identify entries and increased search and transmission loads from 1984 to 8075 requests to access the main database for test cases 3 and 9. These effects depend on the features of the specific encryption algorithm. Therefore, a trade-off between system performance and required security level should be considered to achieve a target performance suitable for the intended application
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Abdul-Jaleel Al-Asady, Heba, et al. “AN IMAGE ENCRYPTION METHOD BASED ON LOGISTICAL CHAOTIC MAPS TO ENCRYPT COMMUNICATION DATA”. Kufa Journal of Engineering, vol. 15, no. 4, Nov. 2024, pp. 55-64, https://doi.org/10.30572/2018/KJE/150405.
Chandel, A., Aggarwal, A., Mittal, A., and Choudhury, T., 2019. Comparative analysis of AES & RSA cryptographic techniques. 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, pp. 410–414. Available at: https://doi.org/10.1109/ICCIKE47802.2019.9004338 [Accessed 20 Nov. 2024].
Chandrasekhar, V.R., et al., 2011. The Stanford mobile visual search data set. Proceedings of the Second Annual ACM Conference on Multimedia Systems, pp. 117–122..
Chen, D.M. and Girod, B., 2014. Memory-efficient image databases for mobile visual search. IEEE MultiMedia, 21(1), pp. 14–23. Available at: https://doi.org/10.1109/MMUL.2013.46 [Accessed 20 Nov. 2024].
Chen, D.M., and Girod, B., 2015. A hybrid mobile visual search system with compact global signatures. IEEE Transactions on Multimedia, 17(7), pp. 1019–1030. Available at: https://doi.org/10.1109/TMM.2015.2427744 [Accessed 20 Nov. 2024].
Chen, J., Duan, L.-Y., Ji, R., Yao, H. and Gao, W., 2011. Sorting local descriptors for low-bit rate mobile visual search. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, pp. 1029–1032. Available at: https://doi.org/10.1109/ICASSP.2011.5946582 [Accessed 20 Nov. 2024].
Chen, X. and Koskela, M., 2011. Mobile visual search from dynamic image databases. Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011, Proceedings 17. Springer, pp. 196–205.
Dagan, J.A., Guy, I. and Novgorodov, S., 2023. Shop by image: Characterizing visual search in e-commerce. Information Retrieval Journal, 26(1), p. 2.
Duan, L.-Y., Ji, R., Chen, Z., Huang, T. and Gao, W., 2014. Towards mobile document image retrieval for digital library. IEEE Transactions on Multimedia, 16(2), pp. 346–359. Available at: https://doi.org/10.1109/TMM.2013.2293063 [Accessed 20 Nov. 2024].
Girod, B., Chandrasekhar, V., Grzeszczuk, R., and Reznik, Y.A., 2011. Mobile visual search: architectures, technologies, and the emerging MPEG standard. IEEE MultiMedia, 18(3), pp. 86–94. Available at: https://doi.org/10.1109/MMUL.2011.48 [Accessed 20 Nov. 2024].
Girod, R.B., et al., 2011. Mobile visual search. IEEE Signal Processing Magazine, 28(4), pp. 61–76. Available at: https://doi.org/10.1109/MSP.2011.940881 [Accessed 20 Nov. 2024].
Gui, Z., Wang, Y., Liu, Y., and Chen, J., 2013. Mobile visual recognition on smartphones. Journal of Sensors, Article ID 2013. Available at: https://doi.org/10.1155/2013/2013 [Accessed 20 Nov. 2024].
Hamza, A., and Kumar, B., 2020. A review paper on DES, AES, RSA encryption standards. 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART), Moradabad, India, pp. 333–338. Available at: https://doi.org/10.1109/SMART50582.2020.9336800 [Accessed 20 Nov. 2024].
Lee, E., et al., 2017. Development of gate security system based on mash-up framework. 2017 Third Asian Conference on Defence Technology (ACDT), Phuket, Thailand, pp. 70–74. Available at: https://doi.org/10.1109/ACDT.2017.7886160 [Accessed 20 Nov. 2024].
Ma, R., Li, J., Guan, H., Xia, M., and Liu, X., 2015. EnDAS: Efficient encrypted data search as a mobile cloud service. IEEE Transactions on Emerging Topics in Computing, 3(3), pp. 372–383. Available at: https://doi.org/10.1109/TETC.2015.2445101 [Accessed 20 Nov. 2024].
Peng, P., Li, J., and Li, Z.-N., 2014. Quality-aware mobile visual search. Procedia-Social and Behavioral Sciences, 147, pp. 383–389.
Pundlik, S., Singh, A., Baghel, G., Baliutaviciute, V. and Luo, G., 2019. A mobile application for keyword search in real-world scenes. IEEE Journal of Translational Engineering in Health and Medicine, 7, pp. 1–10. Available at: https://doi.org/10.1109/JTEHM.2019.2935451 [Accessed 20 Nov. 2024].
Qi, H., Liu, W., and Liu, L., 2017. An efficient deep learning hashing neural network for mobile visual search. 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, Canada, pp. 701–704. Available at: https://doi.org/10.1109/GlobalSIP.2017.8309050 [Accessed 20 Nov. 2024].
R. Qasim, Sara, et al. “A NEW NESTED HYBRID DWT-HD-SVD WATERMARKING SCHEME FOR DIGITAL IMAGES”. Kufa Journal of Engineering, vol. 15, no. 4, Nov. 2024, pp. 65-82, https://doi.org/10.30572/2018/KJE/150406.
Ren, X., et al., 2021. Matching algorithms: fundamentals, applications and challenges. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(3), pp. 332–350. Available at: https://doi.org/10.1109/TETCI.2021.3067655 [Accessed 20 Nov. 2024].
Sharma, S. and Kaushik, B., 2019. A survey on internet of vehicles: applications, security issues & solutions. Vehicular Communications, 20, Art. no. 100182.
Shen, T., Wang, F., Chen, K., Wang, K., and Li, B., 2019. Efficient leveled (multi) identity-based fully homomorphic encryption schemes. IEEE Access, 7, pp. 79299–79310. Available at: https://doi.org/10.1109/ACCESS.2019.2922685 [Accessed 20 Nov. 2024].
Sultan, Nora. “IMAGE COMPRESSION BY USING WALSH AND FRAMELET TRANSFORM ”. Kufa Journal of Engineering, vol. 10, no. 2, June 2021, pp. 27-41, https://doi.org/10.30572/2018/KJE/100203.
Tan, W., Yan, B., Li, K. and Tian, Q., 2016. Image retargeting for preserving robust local feature: Application to mobile visual search. IEEE Transactions on Multimedia, 18(1), pp. 128–137. Available at: https://doi.org/10.1109/TMM.2015.2500727 [Accessed 20 Nov. 2024].
Tsai, S., Chen, H., Chen, D., Vedantham, R., Grzeszczuk, R., and Girod, B., 2011. Mobile visual search using image and text features. 2011 Conference Record of the Forty-Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, CA, USA, pp. 845–849. Available at: https://doi.org/10.1109/ACSSC.2011.6190127 [Accessed 20 Nov. 2024].
Xu, D., Lu, Y., and Li, L., 2021. Embedding blockchain technology into IoT for security: a survey. IEEE Internet of Things Journal, 8(13), pp. 10452–10473. Available at: https://doi.org/10.1109/JIOT.2021.3060508 [Accessed 20 Nov. 2024].
Yang, N., Tang, C., and He, D., 2024. A lightweight certificateless multi-user matchmaking encryption for mobile devices: Enhancing security and performance. IEEE Transactions on Information Forensics and Security, 19, pp. 251–264. Available at: https://doi.org/10.1109/TIFS.2023.3321961 [Accessed 20 Nov. 2024].
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