IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
권호 표지
DOI QR코드

DOI QR Code

Research on Artificial Intelligence Based De-identification Technique of Personal Information Area at Video Data

영상데이터의 개인정보 영역에 대한 인공지능 기반 비식별화 기법 연구

  • In-Jun Song (Graduate School of Information & Communications, Hanbat National University) ;
  • Cha-Jong Kim (Hanbat Nat'l University)
  • Received : 2023.12.13
  • Accepted : 2024.01.03
  • Published : 2024.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

Keywords

References

  1. Y. J. Kim, H. S. Yoon, "Institutional and Policy Issues for the Use and Development of Artificial Intelligence Technology," Korea Institute of Science and Technology Planning and Evaluation, ISSUE PAPER 2016-07, pp. 3-30, 2016. (in Korean) 
  2. K. W. Min, J. D Choi, "Data Collection and Learning Platform for Development of Autonomous Driving Artificial Intelligence Technology," 2020 Korean Society of Automotive Engineers Spring Conference, pp. 456-457, 2020. (in Korean) 
  3. Y. S. Moon, "Video Data Anonymization Technology and Evaluation Method," National Information Society Agency, No. 11, 2019. (in Korean) 
  4. I. H. Jang, "A Study on Measures to Protect Personal Video Information Following the Spread of Mobile Video Information Processing Devices in a High-tech Society," Sungkyunkwan University Law Research Institute, Vol. 28, No. 2, pp. 31-78, 2016. (in Korean)  https://doi.org/10.17008/skklr.2016.28.2.002
  5. J. H. Hong, B. Y. Lee , "Artificial Intelligence-based Security Control Establishment and Response Plan," Korea Contents Association, Vol. 21, No. 1, pp. 531-540, 2021. (in Korean) 
  6. Y. M. Wang, H. Zhang, "Detecting Image Orientation Based on Low-level Visual Content," Computer Vision and Image Understanding, Vol. 93, No. 3, pp. 328-346, 2004.  https://doi.org/10.1016/j.cviu.2003.10.006
  7. E. B. Hong, J. H.Jeon, S. H Cho, Seungyong Lee, "Image Horizontal Correction Using Deep Learning," Korea Computer Graphics Society, Vol. 23, No. 3, pp. 95-103, 2017. (in Korean)  https://doi.org/10.15701/kcgs.2017.23.3.95
  8. B. C. Won, "Mosaic is Required to Export CCTV Footage from Daycare Center... Cost Issue Cannot be Solved," Security News, 2021, 04.23., https://www.boannews.com/media/view.asp?idx=96831 (in Korean) 
  9. D. H Lee, N. J. Park, "A Study on COP-transformation-based Metadata Security Techniques for Personal Information Protection in an Intelligent Video Surveillance Environment," Journal of The Korea Institute of Information Security & Cryptology Vol. 28, No. 2, pp. 417-428, 2018. (in Korean) 
  10. F. Dufaux, T. Ebrahimi, "Scrambling for Privacy Protection in Video Surveillance Systems," IEEE Trans. on Circuits and Systems for Video Technology, Vol. 18, No. 8, pp. 1168-1174, 2008.  https://doi.org/10.1109/TCSVT.2008.928225
  11. F. Dufaux, T. Ebrahimi, "A Framework for the Validation of Privacy Protection Solutions in Video Surveillance," IEEE International Conference on Multimedia and Expo (ICME), pp. 66-71, 2010.