Secure Object Detection Based on Deep Learning


Keonhyeong Kim, Im Young Jung, Journal of Information Processing Systems Vol. 17, No. 3, pp. 571-585, Jun. 2021  

https://doi.org/10.3745/JIPS.03.0161
Keywords: Deep Learning, integrity, Object Detection, Privacy, robustness
Fulltext:

Abstract

Applications for object detection are expanding as it is automated through artificial intelligence-based processing, such as deep learning, on a large volume of images and videos. High dependence on training data and a non-transparent way to find answers are the common characteristics of deep learning. Attacks on training data and training models have emerged, which are closely related to the nature of deep learning. Privacy, integrity, and robustness for the extracted information are important security issues because deep learning enables object recognition in images and videos. This paper summarizes the security issues that need to be addressed for future applications and analyzes the state-of-the-art security studies related to robustness, privacy, and integrity of object detection for images and videos.


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Cite this article
[APA Style]
Kim, K. & Jung, I. (2021). Secure Object Detection Based on Deep Learning. Journal of Information Processing Systems, 17(3), 571-585. DOI: 10.3745/JIPS.03.0161.

[IEEE Style]
K. Kim and I. Y. Jung, "Secure Object Detection Based on Deep Learning," Journal of Information Processing Systems, vol. 17, no. 3, pp. 571-585, 2021. DOI: 10.3745/JIPS.03.0161.

[ACM Style]
Keonhyeong Kim and Im Young Jung. 2021. Secure Object Detection Based on Deep Learning. Journal of Information Processing Systems, 17, 3, (2021), 571-585. DOI: 10.3745/JIPS.03.0161.