Multimedia Tools and Applications

, Volume 77, Issue 7, pp 7811–7850 | Cite as

A SIFT features based blind watermarking for DIBR 3D images

  • Seung-Hun Nam
  • Wook-Hyoung Kim
  • Seung-Min Mun
  • Jong-Uk Hou
  • Sunghee Choi
  • Heung-Kyu Lee


Depth image based rendering (DIBR) is a promising technique for extending viewpoints with a monoscopic center image and its associated per-pixel depth map. With its numerous advantages including low-cost bandwidth, 2D-to-3D compatibility and adjustment of depth condition, DIBR has received much attention in the 3D research community. In the case of a DIBR-based broadcasting system, a malicious adversary can illegally distribute both a center view and synthesized virtual views as 2D and 3D content, respectively. To deal with the issue of copyright protection for DIBR 3D Images, we propose a scale invariant feature transform (SIFT) features based blind watermarking algorithm. To design the proposed method robust against synchronization attacks from DIBR operation, we exploited the parameters of the SIFT features: the location, scale and orientation. Because the DIBR operation is a type of translation transform, the proposed method uses high similarity between the SIFT parameters extracted from a synthesized virtual view and center view images. To enhance the capacity and security, we propose an orientation of keypoints based watermark pattern selection method. In addition, we use the spread spectrum technique for watermark embedding and perceptual masking taking into consideration the imperceptibility. Finally, the effectiveness of the presented method was experimentally verified by comparing with other previous schemes. The experimental results show that the proposed method is robust against synchronization attacks from DIBR operation. Furthermore, the proposed method is robust against signal distortions and typical attacks from geometric distortions such as translation and cropping.


3D image watermarking Depth image based rendering (DIBR) Scale invariant feature transform (SIFT) Blind detection 



This research project was supported by Ministry of Culture, Sports and Tourism(MCST) and from Korea Copyright Commission in 2017.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Seung-Hun Nam
    • 1
  • Wook-Hyoung Kim
    • 1
  • Seung-Min Mun
    • 1
  • Jong-Uk Hou
    • 1
  • Sunghee Choi
    • 1
  • Heung-Kyu Lee
    • 1
  1. 1.School of ComputingKorea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea

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