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Random base image representation for efficient blind vision

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Abstract

Viola-Jones algorithm (Viola and Jones 2001; Int J Comput Vis 57(2):137–154, 2004) is a milestone in the development of face detection technology. It greatly improves the efficiency of face detection on the premise of ensuring the accuracy, which indicates that the research results in the field of computer vision have the ability to put into practical applications. In an application scenario, the client uploads photos to the client server which has trained Viola-Jones detector to detect objects. In the process, privacy leakage of both side is the problem to be solved. Clients want to protect their photos and the cloud server wants to protect its algorithm parameters. Blind Vision (Avidan and Butman 2006), introduced by Avidan & Butman, combined OT(Obvious Transfer) protocol with Viola-Jones face detector to construct a secure face detection protocol. However, the efficiency of Blind Vision is not ideal. It will take a couple of hours to scan a single image. In this paper, we proposed the Random Base Image (RBI) Representation based secure object detection method to speed the process. The original image is divided into several pictures which are sent to the cloud randomly. At the same time, random numbers and redundant fake classifiers are generated to protect privacy parameters of the cloud. Compared with the traditional Blind Vision (Avidan and Butman 2006) method, we did not use OT protocol and Secure Millionaire protocol to calculate feture response of classifiers and compare them with thresholds, but uses random numbers and redundant fake classifiers to protect the privacy of both sides. In addition, the integral-graph can be used to accelerate the calculation of random base images. Experiments show that our method can achieve the same detection accuracy as the original Viola-Jones detector and is much faster than the traditional Blind Vision (Avidan and Butman 2006) method.

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Acknowledgments

Parts of the results and figures presented in this paper have previously appeared in our previous work [14]. We add more technical details and experimental results in this version. This work is partially supported by the National Natural Science Foundation of China (grant numbers 62072014), the Beijing Natural Science Foundation (grant number L192040), the Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety (grant number BTBD- 2018KF-07), Beijing Technology and Business University, the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (grant number VRLAB2019C03), and the Fundamental Research Funds for the Central Universities (grant number. 328201906).

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Authors and Affiliations

  1. Department of Cyber Security, Beijing Electronic Science and Technology Institute, Beijing, 100070, China

    Xin Jin, Hongyu Zhang, Xiaodong Li, Hongbo Sun & Ying Zhang

  2. State Key Laboratory of Cryptology, P.O.Box 5159, Beijing, 100878, China

    Xin Jin

  3. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Mingxue Yu

  4. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China

    Ruijun Liu

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  1. Xin Jin
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  2. Hongyu Zhang
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  3. Xiaodong Li
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  4. Hongbo Sun
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  5. Ying Zhang
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  6. Mingxue Yu
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Correspondence to Xiaodong Li.

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Jin, X., Zhang, H., Li, X. et al. Random base image representation for efficient blind vision. Multimed Tools Appl 80, 7711–7726 (2021). https://doi.org/10.1007/s11042-020-10124-z

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  • DOI: https://doi.org/10.1007/s11042-020-10124-z

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