Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Efficient video matching method for illegal video detection

불법 동영상 검출을 위한 효율적인 동영상 정합 방법

  • Choi, Minseok (Division of AI Informatics, Sahmyook University)
  • 최민석 (삼육대학교 지능정보융합학부)
  • Received : 2021.11.05
  • Accepted : 2022.01.20
  • Published : 2022.01.28
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Abstract

With the development of information and communication technology, the production and distribution of digital contents is rapidly increasing, and the distribution of illegally copied contents also increases, causing various problems. In order to prevent illegal distribution of contents, a DRM (Digital Rights Management)-based approach can be used, but in a situation where the contents are already copied and distributed, a method of searching and detecting the duplicated contents is required. In this paper, a duplication detection method based on the contents of video content is proposed. The proposed method divides the video into scene units using the visual rhythm extracted from the video, and hierarchically applies the playback time and color feature values of each divided scene to quickly and efficiently detect duplicate videos in a large database. Through experiments, it was shown that the proposed method can reliably detect various replication modifications.

정보통신 기술의 발전으로 디지털 콘텐츠의 생산과 유통이 급격히 증가하고 있으며 이와 함께 불법적인 복제 컨텐츠의 유통도 증가하여 여러 문제를 야기하고 있다. 컨텐츠의 불법적인 유통을 막기 위하여 DRM(Digital Rights Management) 기반의 접근 방법을 이용할 수 있지만, 이미 복제되어 유통되는 상황에서는 복제된 컨텐츠를 검색하여 검출하는 방법이 요구된다. 본 논문에서는 동영상 콘텐츠의 내용에 기반한 복제 검출 방법을 제안한다. 제안된 방법은 동영상에서 추출된 비주얼 리듬을 이용하여 동영상을 장면 단위로 분할하고, 분할된 각 장면의 재생 시간과 색상 특징값을 계층적으로 적용하여 대용량 데이터베이스에서 빠르고 효율적으로 복제 동영상 검출이 가능하다. 실험을 통하여 제안된 방법이 다양한 복제 변형에 대하여 안정적 검출이 가능함을 보였다.

Keywords

References

  1. M. R. Kim & T. U. Kim. (2016). A Study on the Effect of Ethical Orientation on Digital Piracy. The Journal of Korean Association of Computer Education, 19(1), 77-86. https://doi.org/10.32431/KACE.2016.19.1.008
  2. B. Choi. (2018). Integrative Analysis on Digital Piracy: Focused on Attitude, Personal Norm, and Habit. The Journal of Society for e-Business Studies, 23(3), 85-109. DOI : 10.7838/jsebs.2018.23.3.085
  3. H. Y. Shen & G. G. Lim. (2018). A Study of the Impacting Factors on Sharing Illegal Digital Contents and Copyright Cognition. Journal of Information Technology Applications & Management, 25(2), 23-40. DOI : 10.21219/jitam.2018.25.2.023
  4. K. J. Park. (2015). A Study on Effects of Relative Benefits and Costs of Piracy of Digital Contents on Attitudes and Behaviors of Illegal Duplication. Journal of the Korea contents association, 15(7), 489-499. DOI : 10.5392/JKCA.2015.15.07.489
  5. S. D. Cheun & G. M. Kang. (2019). A Study on the Identification of the First Person to Distribute Online Pornography through Digital Forensics Analysis - In Focus on Cloud, KakaoTalk, Telegram. The Journal of Police Policies, 33(2), 91-130. DOI : 10.35147/knpsi.2019.33.2.091
  6. B. Y. Sohn & H. S. Suh. (2014). Market condition of Digital contents through interviewing Experts in Business and Research Analysis about License of Individual Contents. Journal of Digital Convergence, 12(12), 357-364. DOI : 10.14400/JDC.2014.12.12.357
  7. D. Cho, S. Hwang & G. Jeong. (2017). DRM Market System for Cloud-based Media Service Platform. Journal of Korea Multimedia Society, 20(6), 918-926. DOI : 10.9717/kmms.2017.20.6.918
  8. J. E. Lee, Y. H. Seo & D. W. Kim. (2020). Deep Learning Framework for Watermark-Adaptive and Resolution-Adaptive Image Watermarking. Journal of broadcast engineering, 25(2), 166-175. DOI : 10.5909/JBE.2020.25.2.166
  9. M. S. Choi & S. W. Choi. (2012). A Study on Video Copy Detection Methods Using Representative Color Sequence for Protecting Copyrights. Journal of Digital Convergence, 10(5), 185-191. https://doi.org/10.14400/JDPM.2012.10.5.185
  10. H. Lee, G. Bae & H. Byun. (2011). Near-duplicate Video Detection and Clustering using Block Difference. Journal of KIISE : Software and Applications, 38(9), 457-502.
  11. Y. Lim, G. Bae, K. Lim, Y. Uh & H. Byun. (2013). Fast Detection of Video Copy using Block Histogram and Dynamic Matching. Journal of KISS : Software and Applications, 40(2), 122-131.
  12. W. Jun, Y. Lee & B. M. Jun. (2016). Duplicate video detection for large-scale multimedia. Multimedia Tools and Applications, 75(23), 15665-15678. DOI : 10.1007/s11042-015-2724-0
  13. K. R. Kim, J. T. Lee, W. D. Jang & C. S. Kim. (2015). Frame-level Matching for Near Duplicate Videos Using Binary Frame Descriptor. Journal of Broadcast Engineering, 20(4), 641-644. DOI : 10.5909/JBE.2015.20.4.641
  14. S.J.F. Guimar, M. Couprie, N.J. Leite & D. A. Araujo. (2001, Oct). A method for cut detection based on visual rhythm. Proceedings XIV Brazilian Symposium on Computer Graphics and Image Processing. (pp. 297-304). Florianopolis : IEEE. DOI : 10.1109/SIBGRAPI.2001.963069
  15. J. M. Chung & C. G. Kim. (2011). An Efficient Video Indexing Scheme Exploiting Visual Rhythm. Journal of information and telecommunication facility engineering, 10(3), 103-109.
  16. M. R. Souza, H. A. Maia, M. B. Vieira & H. Pedrini. (2020). Survey on visual rhythms: A spatio-temporal representation for video sequences. Neurocomputing, 402, 409-422. DOI : 10.1016/j.neucom.2020.04.035
  17. A. Talib, M. Mahmuddin, H. Husni & L. E. George. (2013). A weighted dominant color descriptor for content-based image retrieval. Journal of Visual Communication and Image Representation, 24(3), 345-360. DOI : 10.1016/j.jvcir.2013.01.007
  18. G. Xie, B. Guo, Z. Huang, Y. Zheng & Y. Yan. (2020). Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval. IEEE Access, 8, 146284-146299. DOI : 10.1109/ACCESS.2020.3015285