한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제20권10호
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  • Pages.1619-1627
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  • 2017
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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Perceptual Bound-Based Asymmetric Image Hash Matching Method

  • Seo, Jiin Soo (Dept. of Electrical Eng., Gangneung-Wonju National University)
  • 투고 : 2017.07.04
  • 심사 : 2017.08.28
  • 발행 : 2017.10.31
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초록

Image hashing has been successfully applied for the problems associated with the protection of intellectual property, management of large database and indexation of content. For a reliable hashing system, improving hash matching accuracy is crucial. In order to improve the hash matching performance, we propose an asymmetric hash matching method using the psychovisual threshold, which is the maximum amount of distortion that still allows the human visual system to identity an image. A performance evaluation over sets of image distortions shows that the proposed asymmetric matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.

키워드

참고문헌

  1. T. Kalker, J. Haitsma, and J. Oostveen, "Issues with Digital Watermarking and Perceptual Hashing," Proceeding of Society of Photo- Optical Instrumentation Engineers, Multimedia Systems and Applications IV, pp. 189, 2001.
  2. A. Menezes, P. Oorshot, and S. Vanstone, Handbook of Applied Cryptography, Chemical Rubber Company Press, Boca Raton, Florida, 1997.
  3. M. Kim, M. Park, and H. Kim, “Automatic Music Summarization Method by Using the Bit Error Rate of the Audio Fingerprint and a System Thereof,” Journal of Korea Multimedia Society, Vol. 16, No. 4, pp. 453-463, 2013. https://doi.org/10.9717/kmms.2013.16.4.453
  4. B. Girod, "The Information Theoretical Significance of Spatial and Temporal Masking in Video Signals," Proceeding of Society of Photo-Optical Instrumentation Engineers, Human Vision, Visual Processing, and Digital Display, pp. 178-187, 1989.
  5. N. Jayant, J. Johnston, and R. Safranek, “Signal Compression Based on Models of Human Perception,” Proceedings of the IEEE, Vol. 81, No. 10, pp. 1385-1422, 1993. https://doi.org/10.1109/5.241504
  6. J. Seo, “An Asymmetric Matching Method for a Robust Binary Audio Fingerprinting,” IEEE Signal Processing Letters, Vol. 21, No. 7, pp. 844-847, 2014. https://doi.org/10.1109/LSP.2014.2310237
  7. W. Dong, M. Charikar, and K. Li, "Asymmetric Distance Estimation with Sketches for Similarity Search in High-dimensional Spaces," Proceeding of ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 123-130, 2008.
  8. H. Jegou, M. Douze, and C. Schmid, “Product Quantization for Nearest Neighbor Search,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 1, pp. 117- 128, 2011. https://doi.org/10.1109/TPAMI.2010.57
  9. A. Gordo, F. Perronnin, Y. Gong, and S. Lazebnik, “Asymmetric Distances for Binary Embeddings,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 1, pp. 33-47, 2014. https://doi.org/10.1109/TPAMI.2013.101
  10. A. Varna, A. Swaminathan, and M. Wu, "A Decision Theoretic Framework for Analyzing Binary Hash-based Content Identification Systems," Proceeding of ACM Workshop on Digital Rights Management, pp. 67-76, 2008.
  11. J. Fridrich, "Robust Bit Extraction from Images," Proceeding of IEEE Conference on Multimedia Computing and Systems, pp. 536-540, 1999.
  12. R. Venkatesan, S. Koon, M.H. Jakubowski, and P. Moulin, "Robust Image Hashing," Proceeding of IEEE Conference on Image Processing, pp. 664-666, 2000.
  13. X. Lv and Z. Wang, “Perceptual Image Hashing Based on Shape Contexts and Local Feature Points,” IEEE Transactions on Information Forensics and Security, Vol. 7, No. 3, pp. 1081-1093, 2012. https://doi.org/10.1109/TIFS.2012.2190594
  14. J. Seo and M. Jo, “A Probabilistic Dissimilarity Matching for the DFT-domain Image Hashing,” International Journal of Advanced Culture Technology, Vol. 5, No. 1, pp. 76-82, 2017. https://doi.org/10.17703/IJACT.2017.5.1.76
  15. S. Mallat, “A Theory for Multiresolution Signal Decomposition: the Wavelet Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, pp. 674- 693, 1989. https://doi.org/10.1109/34.192463
  16. J. Fridrich and M. Goljan, Spatial Image Masking Code, http://www.ws.binghamton.edu/fridrich/ masking.html (accessed Oct., 23, 2017).
  17. R. Barnett and D. Pearson, “Frequency Mode LR Attack Operator for Digitally Watermarked Images,” Electronics Letters, Vol. 34, No. 19, pp. 1837-1839, 1998. https://doi.org/10.1049/el:19981323
  18. S. Pereira, S. Voloshynovskiy, M. Madueno, S. Marchand-Maillet, and T. Pun, "Second Generation Benchmarking and Application Oriented Evaluation," Proceeding of Information Hiding Workshop, pp. 340-353, 2001.
  19. S. Voloshynovskiy, T. Holotyak, O. Koval, F. Beekhof, and F. Farhadzadeh, "Sign-magnitude Decomposition of Mutual Information with Polarization Effect in Digital Identification," Proceeding of IEEE Information Theory Workshop, pp. 385-389, 2011.

피인용 문헌

  1. 파워마스크를 이용한 영상 핑거프린트 정합 성능 개선 vol.23, pp.1, 2017, https://doi.org/10.9717/kmms.2020.23.1.008