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Protection of image ROI using chaos-based encryption and DCNN-based object detection

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Abstract

Images always contain sensitive information, e.g., a clear face on a photo, which needs to be protected. The simple way is to encrypt the whole image for hiding “everything” securely, but it brings huge amounts of unnecessary encryption operations. Considering the most sensitive regions of an image, this paper focuses on protecting the important regions, thus reducing the redundant encryption operations. This paper employs the latest DCNN-based object detection model (YOLOv4) for choosing regions (i.e., multiple objects) and chaos-based encryption for fast encryption. We analyze object detection algorithm from a security perspective and modify YOLOv4 to guarantee that all areas of the detected objects are contained in the output regions of interest (ROI). Later, we propose a multi-object-oriented encryption algorithm to protect all the detected ROI at one go. We also encrypt the ROI coordinates and embed them into the whole image, relieving the burden of distributing ROI coordinates separately. Experimental results and security analyses show that all the detected objects are well protected.

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Notes

  1. In response to an anonymous reviewer: Authors at CUHK and NEU thanks him/her for motivating their discussion on potentially-further works, i.e., protecting detectable-and-sensitive objects with reasonable access control and fast 2D chaotic encryption.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61773068), the National Key R&D Program of China (No. 2021YFF0306405), and the Fundamental Research Funds for the Central Universities (No. N2024005-1).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Wei Song & Chong Fu

  2. Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, China

    Chong Fu

  3. Department of Information Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China

    Yu Zheng

  4. School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, 100101, China

    Lin Cao

  5. Science and Technology on Space Physics Laboratory, Beijing, 100076, China

    Ming Tie

  6. School of Computer Science, The University of Auckland, Auckland, New Zealand

    Chiu-Wing Sham

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  1. Wei Song
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  2. Chong Fu
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  3. Yu Zheng
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  4. Lin Cao
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  5. Ming Tie
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Correspondence to Chong Fu.

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Song, W., Fu, C., Zheng, Y. et al. Protection of image ROI using chaos-based encryption and DCNN-based object detection. Neural Comput & Applic 34, 5743–5756 (2022). https://doi.org/10.1007/s00521-021-06725-w

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