Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

ENF based Detection of Forgery and Falsification of Digital Files due to Quadratic Interpolation

이차 보간에 따른 ENF 기반의 위변조 디지털 파일 탐지 기법

  • 박세진 (고려대학교 정보보호대학원) ;
  • 윤지원 (고려대학교 정보보호대학원)
  • Received : 2017.09.12
  • Accepted : 2018.01.10
  • Published : 2018.03.15
⟨ Previous Next ⟩

Abstract

Recently, the use of digital audio and video as proof in criminal and all kinds of litigation is increasing, and scientific investigation using digital forensic technique is developing. With the development of computing and file editing technologies, anyone can simply manipulate video files, and the number of cases of manipulating digital data is increasing. As a result, the integrity of the evidence and the reliability of the evidence Is required. In this paper, we propose a technique for extracting the Electrical Network Frequency (ENF) through a grid of power grids according to the geographical environment for power supply, and then performing signal processing for peak detection using QIFFT. Through the detection algorithm using the standard deviation, it was confirmed that the video file was falsified with 73% accuracy and the forgery point was found.

최근 형사 및 모든 분야의 소송에서 디지털 오디오 및 비디오를 증거로써 사용하는 경우가 증가하고 있으며, 이에 디지털 포렌식 기법을 이용한 과학 수사가 발전하고 있다. 컴퓨팅 기능과 파일 편집 기술의 발달로 누구나 간단하게 비디오 파일을 조작할 수 있게 되면서 디지털 데이터를 조작하는 사례는 증가하고 있으며, 이로 인해 디지털 데이터에 대한 감정을 통해 증거의 무결성과 신뢰성을 확보하는 일이 요구되고 있다. 본 연구에서는 디지털 포렌식 기법 중 하나로 전력 공급에 대한 지리적 환경에 따른 전력망 그리드를 통해 전력망 주파수 신호(Electrical Network Frequency: ENF)를 추출하고 QIFFT를 이용해 peak 검출을 위한 신호처리 과정을 거치는 기법에 대해 제안한다. 그리고 표준편차를 이용한 탐지 알고리즘을 통해 73%의 정확도로 비디오 파일의 위변조 여부 확인 및 위변조 지점을 찾는 실험을 진행하고 이를 검증하였다.

Keywords

Acknowledgement

Supported by : 국방과학연구소

References

  1. Digital forensic technology trends and domestic competitiveness (source : IITP)
  2. [Online]. Available: http://forensic-proof.com/archives/4689
  3. Kimin Seo, Dongchan Lee, Jongsung Kim and Sangjin Lee, 2009, "Detecting Similar Multimedia Files for Digital Forensic Investigation," pp. 366-368.
  4. Seyol Yang, Hyuk Choi "Forensic Watermarking for Digital Cinema Using Temporal Chrominance Differences," pp. 81-87, Dec. 2008.
  5. GARG, Ravi; HAJJ-AHMAD, Adi; WU, Min, Geolocation estimation from Electrical Network Frequency signals, ICASSP, pp. 2862-2866, 2013.
  6. SU, Hui, et al., Enf signal induced by power grid: A new modality for video synchronization, Proc. of the 2nd ACM International Workshop on Immersive Media Experiences, ACM, pp. 13-18, 2014.
  7. CHAI, Jidong, et al., Source of ENF in batterypowered digital recordings, Audio Engineering Society Convention 135, Audio Engineering Society, 2013.
  8. GUO, Jiahui, et al., Events associated power system oscillations observation based on distribution-level phasor measurements, T&D Conference and Exposition, 2014 IEEE PES, IEEE, pp. 1-5, 2014.
  9. GRIGORAS, Catalin. Applications of ENF criterion in forensic audio, video, computer and telecommunication analysis, Forensic science international, 167.2: 136-145, 2007. https://doi.org/10.1016/j.forsciint.2006.06.033
  10. ABE, Mototsugu; SMITH III, Julius O. Design criteria for the quadratically interpolated FFT method (I): Bias due to interpolation. no. STAN-M-114, 2004.
  11. COOPER, Alan J., An automated approach to the Electric Network Frequency (ENF) criterion-Theory and practice, International Journal of Speech, Language and the Law, Vol. 16, No. 2, pp. 193-218, 2009.
  12. SU, Hui, et al., ENF analysis on recaptured audio recordings, Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, pp. 3018-3022, 2013.
  13. ZHI-SHENG, Lu, et al., A Blind Audio Forgery Detection Algorithm Based on Dual Judgment Mechanisms, Journal of South China University of Technology (Natural Science Edition), 8: 013, 2014.