DOI QR코드

DOI QR Code

Blind Digital Watermarking Methods for Omni-directional Panorama Images using Feature Points

특징점을 이용한 전방위 파노라마 영상의 블라인드 디지털 워터마킹 방법

  • Kang, I-Seul (Department of Electronic Materials Engineering, Kwangwoon University) ;
  • Seo, Young-Ho (Ingenium College of Liberal Arts, Kwangwoon University) ;
  • Kim, Dong-Wook (Department of Electronic Materials Engineering, Kwangwoon University)
  • 강이슬 (광운대학교 전자재료공학과) ;
  • 서영호 (광운대학교 인제니움학부대학) ;
  • 김동욱 (광운대학교 전자재료공학과)
  • Received : 2017.08.22
  • Accepted : 2017.09.12
  • Published : 2017.11.30

Abstract

One of the most widely used image media in recent years, omni-directional panorama images are attracting much attention. Since this image is ultra-high value-added, the intellectual property of this image must be protected. In this paper, we propose a blind digital watermarking method for this image. In this paper, we assume that the owner of each original image may be different, insert different watermark data into each original image, and extract the watermark from the projected image, which is a form of service of omni- directional panorama image. Therefore, the main target attack in this paper is the image distortion which occurs in the process of the omni- directional panorama image. In this method, SIFT feature points of non-stitched areas are used, and watermark data is inserted into data around each feature point. We propose two methods of using two-dimensional DWT coefficients and spatial domain data as data for inserting watermark. Both methods insert watermark data by QIM method. Through experiments, these two methods show robustness against the distortion generated in the panorama image generation process, and additionally show sufficient robustness against JPEG compression attack.

최근 가장 넓은 응용분야를 갖고 있는 영상매체 중 하나로 전방위 파노라마 영상이 큰 관심을 받고 있다. 이 영상은 초부가가치를 갖고 있기 때문에 이 영상의 지적재산권이 보호되어야 한다. 본 논문에서는 이를 위해 이 영상에 대한 블라인드 디지털 워터마킹 방법을 제안한다. 본 논문에서는 촬영된 원영상 각각의 소유권자가 다를 수 있다고 가정하고, 각 원영상에 서로 다른 워터마크 데이터를 삽입하고, 전방위 파노라마 영상이 서비스되는 형태인 투영된 영상에서 워터마크를 추출하는 것으로 하였다. 따라서 본 논문에서의 주된 타겟 공격은 전방위 파노라마 영상을 생성하는 과정에서 발생하는 영상왜곡이다. 그 방법에 있어서, 스티칭에 사용되지 않는 영역의 SIFT 특징점들을 사용하며, 각 특징점 주위의 데이터에 워터마크 데이터를 삽입한다. 워터마크를 삽입하는 데이터로 2차원 DWT 계수와 공간영역 데이터를 사용하는 두 가지 방법을 제안하는데, 두 방법 모두 QIM방식으로 워터마크 데이터를 삽입한다. 실험을 통하여 이 두 방법이 파노라마 영상 생성과정에서 발생하는 왜곡에 강인함을 보이며, 추가적으로 JPEG 압축 공격에도 충분한 강인성을 보인다.

Keywords

References

  1. H. Abdelkader, Hicham, E. Malis, and P. Rives. "Spherical image processing for accurate visual odometry with omnidirectional cameras." The 8th Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras-OMNIVIS. 2008.
  2. S. E. Chen, "QuickTime VR - An image based approach to virtual environment navigation," in Proceedings of the ACM SIGGRAPH, pp. 29-38, ACM, (New York), 1995
  3. J. Cox, M. Miller, J. Bloom and T. Kalker, Digital watermarking and steganography, 2nd Ed., Elsevier, 2008.
  4. B., Matthew, and D. G. Lowe. "Automatic panoramic image stitching using invariant features." International journal of computer vision Vol. 74, No. 1, pp. 59-73, Aug. 2007 https://doi.org/10.1007/s11263-006-0002-3
  5. Mulcahy, Karen A., and Keith C. Clarke. "Symbolization of map projection distortion: a review." Cartography and geographic information science Vol. 28, No. 3, pp. 167-182, 2001 https://doi.org/10.1559/152304001782153044
  6. X. Ye, X. Cheng, M. Deng, and Y. Wang, "A SIFT-based DWT-SVD Blind Watermark Method Against Geometrical Attacks," IEEE ICISP, pp. 323-329, Oct. 2014.
  7. H-T. Hu, Y-J. Chnag, and S-H. Chen, "A progress QIM to cope with SVD-based blind image watermarking in DWT domain," IEEE China SIP, pp. 421-425, July 2014.
  8. J. Ouyang, G. Goatreux, B. Chen, and H.Shu, "Color image water-marking based on quaternion Fourier Transform and improved uniform log-polar mapping," Computers & Electrical Engineering Vol. 46, pp. 419-432, 2015 https://doi.org/10.1016/j.compeleceng.2015.03.004
  9. Y-S Lee, Y-H Seo and D-W Kim, "A Robust Blind Watermarking for Digital Image Using DWT According to its Resolution," JOURNAL OF BROADCAST ENGINEERING, Vol. 20, No. 6, pp. 888-900, Nov. 2015. https://doi.org/10.5909/JBE.2015.20.6.888
  10. Y-H. Lin and J-L Wu, "A digital blind watermarking for depth-image-based rendering 3D images," IEEE Trans. on Broadcasting, Vol. 57, No. 2, pp. 602-611, June 2011. https://doi.org/10.1109/TBC.2011.2131470
  11. H-D Kim, and J-W Lee, "Robust DT-CWT Watermarking for DIBR 3D Images." IEEE Transactions on Broadcasting, Vol. 58, No. 4, pp. 533-543, Oct. 2012. https://doi.org/10.1109/TBC.2012.2206851
  12. S. Wang, C. Cui, and X. Niu, "Watermarking for DIBR 3D images based on SIFT feature points," Elsevier Measurement 48, pp. 54-62, 2014 https://doi.org/10.1016/j.measurement.2013.10.028
  13. Y-S Lee, Y-H Seo and D-W Kim, "Robust and Blind Watermarking for DIBR Using a Depth Variation Map," JOURNAL OF BROADCAST ENGINEERING, Vol. 21, No. 6, pp. 845-860, Nov. 2016. https://doi.org/10.5909/JBE.2016.21.6.845
  14. D. G. Lowe, "Distinctive image features from scale-invariant key-points," Int'l J. of Computer Vision, Vol. 60, No. 2, pp. 91-110, Nov. 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  15. H. Bay, T. Tuytelaars, and L. V. Gool, "SURF:Speeded up robust features," ECCV, pp. 404-417, 2006.
  16. E. Rosten and T. Drummond, "Machine learning for high-speed corner detection." ECCV, pp. 430-443, 2006.
  17. http://hugin.sourceforge.net/docs/manual/Projections.html
  18. https://www.cnet.com/products/gopro-hero4-black/
  19. http://www.kolor.com/autopano/autopano-features/
  20. Christof Paar, Jan Pelzl, Understanding Cryptography, A Textbook for Students and Practitioners, Springer, 2009.
  21. B. Chen, W. and Wornell, "Quantization Index Modulation: A Class of provably good methods for digital watermarking and information embedding," IEEE Trans. on Information Theory, Vol. 47, No. 4, pp. 1423-1442, May 2001. https://doi.org/10.1109/18.923725
  22. R. C. Gonzalez and R. E. Woods, Digital image processing, 3rd Ed., 2008.