Journal of Information Processing Systems

  • 제17권3호
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  • Pages.571-585
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  • 2021
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Secure Object Detection Based on Deep Learning

  • Kim, Keonhyeong (School of Electronics and Engineering, Kyungpook National University) ;
  • Jung, Im Young (School of Electronics and Engineering, Kyungpook National University)
  • 투고 : 2020.03.20
  • 심사 : 2020.10.11
  • 발행 : 2021.06.30
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⟨ 이전 논문 다음 논문 ⟩

초록

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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과제정보

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Korea (No. 2017R1D1A1B03034950).

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