The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Imaging Device Identification using Sensor Pattern Noise Based on Wiener Filtering

Wiener 필터링에 기반하는 센서 패턴 노이즈를 활용한 영상 장치 식별 기술 연구

  • Lee, Hae-Yeoun (Dept. of Computer Software Engineering, Kumoh National Institute of Technology)
  • Received : 2016.10.13
  • Accepted : 2016.11.17
  • Published : 2016.12.01
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Abstract

Multimedia such as image, audio, and video is easy to create and distribute with the advance of IT. Since novice uses them for illegal purposes, multimedia forensics are required to protect contents and block illegal usage. This paper presents a multimedia forensic algorithm for video to identify the device used for acquiring unknown video files. First, the way to calculate a sensor pattern noise using Wiener filter (W-SPN) is presented, which comes from the imperfection of photon detectors against light. Then, the way to identify the device is explained after estimating W-SPNs from the reference device and the unknown video. For the experiment, 30 devices including DSLR, compact camera, smartphone, and camcorder are tested and analyzed quantitatively. Based on the results, the presented algorithm can achieve the 96.0% identification accuracy.

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References

  1. J. Lukas, J. Fridrich, and M. Goljan, "Digital camera identification from sensor pattern noise," IEEE Transactions on Information Forensics Security, vol. 1, no. 2, pp. 205-214, June 2006. https://doi.org/10.1109/TIFS.2006.873602
  2. M. Chen, J. Fridrich, and M. Goljan, "Source digital camcorder identification using ccd photo response nonuniformity," Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, San Jose, CA, pp. 1G-1H, 2007.
  3. S. Bayram, H. T. Sencar, N. Memon, and I. Avcibas "Source Camera Identification Based on CFA Interpolation," Proceedings of IEEE Int. Conf. on Image Processing (ICIP), pp. 69-72, 2005.
  4. D.-K. Hyun, S.-J. Ryu, H.-Y. Lee, and H.-K. Lee, "Detection of Upscale-Crop and Partial Manipulation in Surveillance Video based on Sensor Pattern Noise," Sensors, vol. 13, no. 9, pp. 12605-12631, Sep. 2013. https://doi.org/10.3390/s130912605
  5. A. C. Popescu and H. Farid, "Exposing Digital Forgeries by Detecting Traces of Resampling," IEEE Transactions on Signal Processing, vol. 53, no. 2, pp. 758-767, Feb. 2005. https://doi.org/10.1109/TSP.2004.839932
  6. W. Wang and H. Farid, "Exposing Digital Forgeries in Video by Detecting Double MPEG Compression," Proceedings of the 8th workshop on ACM Multimedia and security, pp. 37-47, 2006.
  7. C. H. Choi, H.-Y. Lee, and H.-K. Lee, "Estimation of Color Modification in Digital Images by CFA Pattern Change," Forensic Science International, vol. 226, no. 1-3, pp. 94-105, March 2013. https://doi.org/10.1016/j.forsciint.2012.12.014
  8. C.-T. Li, "Source Camera Identification Using Enhanced Sensor Pattern Noise," IEEE Transactions on Information Forensics Security, vol. 5, no. 2, pp. 280-287, June 2010. https://doi.org/10.1109/TIFS.2010.2046268