Depth Information Estimation-Based DIBR 3D Image Hashing Using SIFT Feature Points

  • Chen Cui
  • Shen WangEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)


Image hashing has been widely used for traditional 2D image authentication, content-based identification, and retrieval. Being different from the traditional 2D image system, virtual image pair is generated from the center image according to the corresponding depth image in the DIBR process. In one of the communication models for DIBR 3D image system, the content consumer side only receives the virtual images without performing DIBR operation. By this way, only a variety of copies for virtual image pairs could be distributed. This paper designs a novel DIBR 3D image hashing scheme based on depth information estimation using local feature points, by detecting the matched feature points in virtual image pair and dividing these feature points into different groups according to the corresponding depth information estimated to generate the hash vector. As the experiments shown, the proposed DIBR 3D image hashing is robust against most of the content-preserving operations.


Depth image-based rendering (DIBR) DIBR 3D image hashing DIBR 3D image identification Depth information estimation 



This work is supported by the National Natural Science Foundation of China (Grant Number: 61702224).


  1. 1.
    Fehn, C.: Depth-image-based rendering (DIBR) compression and transmission for a new approach on 3D-TV. In: Proceedings of the SPIE Stereoscopic Displays and Virtual Reality Systems XI, pp. 93–104 (2004)Google Scholar
  2. 2.
    Chen, C.M., Xu, L.L, Wu, T.S., Li, C.R: On the security of a chaotic maps-based three-party authenticated key agreement protocol. J. Netw. Intell. 1(2), 61–66 (2016)Google Scholar
  3. 3.
    Chen, C.M., Huang, Y.Y., Wang, Y.K., Wu, T.S.: Improvement of a mutual authentication protocol with anonymity for roaming service in wireless communications. Data Sci. Pattern Recognit. 2(1), 15–24 (2018)Google Scholar
  4. 4.
    Ahmed, F., Siyal, M.Y., Abbas, V.U.: A secure and robust hash-based scheme for image authentication. Signal Process. 90(5), 1456–1470 (2010)zbMATHCrossRefGoogle Scholar
  5. 5.
    Monga, V., Evans, B.L.: Perceptual image hashing via feature points: performance evaluation and tradeoffs. IEEE Trans. Image Process. 15(11), 3452–3465 (2006)CrossRefGoogle Scholar
  6. 6.
    Kozat, S., Venkatesan, R., Mihcak, M.: Robust perceptual image hashing via matrix invariants. In: 2004 International Conference on Image Processing, pp. 3443–3446. IEEE, Singapore, Singapore (2004)Google Scholar
  7. 7.
    Monga, V., Mhcak, M.K.: Robust and secure image hashing via non-negative matrix factorizations. IEEE Trans. Inf. Forensics Secur. 2(3), 376–390 (2007)CrossRefGoogle Scholar
  8. 8.
    Roy, S., Sun, Q.: Robust hash for detecting and localizing image tampering. In: 2007 IEEE International Conference on Image Processing, pp. 117–120. IEEE, San Antonio, TX, USA (2007)Google Scholar
  9. 9.
    Lv, X., Wang, Z.J.: Perceptual image hashing based on shape contexts and local feature points. IEEE Trans. Inf. Forensics Secur. 7(3), 1081–1093 (2012)CrossRefGoogle Scholar
  10. 10.
    Tang, Z.J., Zhang, X.Q., Li, X.X., Chao, S.C.: Robust image hashing with ring partition and invariant vector distance. IEEE Trans. Inf. Forensics Secur. 11(1), 200–214 (2016)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Zhang, L., Tam, W.: Stereoscopic image generation based on depth images for 3d TV. IEEE Trans. Broadcast. 51(2), 191–199 (2015)CrossRefGoogle Scholar
  13. 13.
    Scharstein, D., Pal, C.: Learning conditional random fields for stereo. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE, Minneapolis, MN, USA (2007)Google Scholar
  14. 14.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2007)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information Science and TechnologyHeilongjiang UniversityHarbinChina
  2. 2.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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