IRIW: Image Retrieval Based Image Watermarking for Large-Scale Image Databases

  • Jong Yun Jun
  • Kunho Kim
  • Jae-Pil Heo
  • Sung-eui Yoon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7128)


We present a novel, Image Retrieval based Image Watermark (IRIW) framework to identify copyright-violated images in both efficient and accurate manner for large-scale image databases. We first perform SIFT-based image retrieval to identify similar images given a query image and store them as an output list. Then we extract watermark patterns and check watermark similarity only for images stored in the list. As a final step, we re-rank images by considering various information available between each image in the list and the query image and by utilizing information even among images in the list. Also, in order to reduce any negative impacts on image retrieval by embedding watermark patterns on images, we propose to use a SIFT-aware image watermark detection method. Compared with the exhaustive method that checks all the images stored in an image database that consists of 10 K images, our method achieves more than two orders of magnitude performance improvement. More importantly, by identifying similar images given a query image and focusing on checking watermark similarities among those similar images, we are able to reduce false positive and false negative cases by a factor of up to two over the exhaustive method.


Image Retrieval Visual Word Image Watermark Query Image Scale Invariant Feature Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.: An optimal algorithm for approximate nearest neighbor searching. In: Symp. on Discrete Alg., pp. 573–582 (1994)Google Scholar
  2. 2.
    Bas, P., Chassery, J.-M., Macq, B.: Geometrically invariant watermarking using feature points. IEEE Trans. on Image Processing 11, 1014–1028 (2002)CrossRefGoogle Scholar
  3. 3.
    de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.: Computational Geometry: Algorithms and Applications. Springer-Verlag TELOS, Santa Clara (2008)zbMATHGoogle Scholar
  4. 4.
    Berrani, S.A., Amsaleg, L., Gros, P.: Robust content-based image searches for copyright protection. In: Proceedings of the 1st ACM International Workshop on Multimedia Databases, pp. 70–77 (2003)Google Scholar
  5. 5.
    Chum, O., Philbin, J., Isard, M., Zisserman, A.: Scalable near identical image and shot detection. In: ACM International Conference on Image and Video Retrieval, pp. 549–556 (2007)Google Scholar
  6. 6.
    Cox, I.J., Kilian, J., Leighton, F.T., Shamoon, T.: Secure spread spectrum watermarking for multimedia. IEEE Transactions on Image Processing 6(12), 1673–1687 (1997)CrossRefGoogle Scholar
  7. 7.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Survey 40(2), 1–60 (2008)CrossRefGoogle Scholar
  8. 8.
    Huston, L., Sukthankar, R., Ke, Y.: Evaluating keypoint methods for content-based copyright protection of digital images. In: IEEE International Conference on Multimedia and Expo (ICME), p. 4 (July 2005)Google Scholar
  9. 9.
    Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: CVPR, pp. 3304–3311 (2010)Google Scholar
  10. 10.
    Lee, H.Y., Kim, H.S., Lee, H.K.: Robust image watermarking using invariant features. Optical Engineering 45(3), 1–11 (2006)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Lu, Z.-M., Skibbe, H., Burkhardt, H.: Image Retrieval Based on a Multipurpose Watermarking Scheme. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3682, pp. 573–579. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: CVPR, pp. 2161–2168 (2006)Google Scholar
  14. 14.
    Petitcolas, F.: Watermarking schemes evaluation. IEEE Signal Processing Magazine 17(5), 58–64 (2000)CrossRefGoogle Scholar
  15. 15.
    Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR, pp. 1–8 (2007)Google Scholar
  16. 16.
    Piva, A., Barni, M., Bartolini, F., Cappellini, V.: Dct-based watermark recovering without resorting to the uncorrupted original image. In: ICIP, p. 520 (1997)Google Scholar
  17. 17.
    Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: ICCV, vol. 2, pp. 1470–1477 (2003)Google Scholar
  18. 18.
    Tang, C.W., Hang, H.M.: A feature-based robust digital image watermarking scheme. IEEE Trans. on Signal Processing 51(4), 950–959 (2003)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Xu, J., Hua Qin, W., Ying Ni, M.: A new scheme of image retrieval based upon digital watermarking. In: Int. Symp. on Computer Science and Computational Tech., pp. 617–620 (2008)Google Scholar
  20. 20.
    Zhao, W.L., Ngo, C.W.: Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection. IEEE Transactions on Image Processing 18(2), 412–423 (2009)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Zheng, D., Liu, Y., Zhao, J., Saddik, A.E.: A survey of rst invariant image watermarking algorithms. ACM Computing Survey 39(2), 5 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jong Yun Jun
    • 1
  • Kunho Kim
    • 1
  • Jae-Pil Heo
    • 1
  • Sung-eui Yoon
    • 1
    • 2
  1. 1.Dept. of Computer ScienceKAISTSouth Korea
  2. 2.Div. of Web Sci. and Tech.KAISTSouth Korea

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