Abstract
Image and multimedia data produced by individuals and enterprises is increasing in recent years. The need of outsourcing such intensive image feature detection tasks to cloud computing continues to grow. However, the concerns over the effective protection of private image and multimedia data when outsourcing it to cloud platform become the major barrier that impedes the further implementation of cloud computing techniques over massive amount of image and multimedia data. To address this challenge, a new scheme that supports Content Based Image Retrieval (CBIR) over the encrypted images without revealing the sensitive information to the cloud server is proposed. The novel scheme is based on complex networks theory and Speeded Up Robust Features (SURF) technique. SURF is one of the important local feature detection algorithms and has been broadly employed in different areas, including object recognition, image matching, robotic mapping, and so on. A high-performance privacy-preserving SURF feature detection system is analyzed and modeled for the secure computation purpose. The coordinates of the key-points (features) obtained from SURF detector are used to generate the complex network work. The security analysis and experiments show the security and efficiency of the proposed scheme.
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Acknowledgments
This paper is one of the results of the 2016 Guangdong Provincial Higher Vocational Education Leading Talent Project (Hui Suo). This work is partly supported by the National Natural Science Foundation of China (NO. 51505191) and the Chinese Postdoctoral Science Foundation (NO. 2018M632229).
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Liu, Z., Huang, C., Suo, H., Yang, B. (2019). A Novel Content Based Image Retrieval Scheme in Cloud Computing. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_52
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DOI: https://doi.org/10.1007/978-3-030-24265-7_52
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