Abstract
In this paper, we propose a robust perceptual hashing algorithm by using video luminance histogram in shape. The underlying robustness principles are based on three main aspects: 1) Since the histogram is independent of position of a pixel, the algorithm is resistant to geometric deformations; 2) the hash is extracted from the spatial Gaussian-filtering low-frequency component for those common video processing operations such as noise corruption, low-pass filtering, lossy compression, etc.; 3) a temporal Gaussian-filtering operation is designed so that the hash is resistant to temporal desynchronization operations, such as frame rate change and dropping. As a result, the hash function is robust to common geometric distortions and video processing operations. Experimental results show that the proposed hashing strategy can provide satisfactory robustness and uniqueness.
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References
Hamon K, Schmucker M, Zhou X. Histogram-based perceptual hashing for minimally changing video sequences. In: Proceedings of the Second International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution, Leeds, 2006. 236–241
Harris C, Stephens M J. A combined corner and edge detector. In: Proceedings of the Fourth Alvey Vision Conference, University of Manchester, 1988. 147–152
Zhou Y, Liu G, Dai Y, et al. Robust hashing based on persistent points for video copy detection. In: 2008 International Conference on Computational Intelligence and Security. Suzhou, China, 2008. 305–308
Zhou X, Schmucker M, Brown C L. Perceptual hashing of video content based on differential block similarity. Lecture Notes in Computer Science, 2005, 3802: 80–85
Lee S, Yoo C D. Robust video fingerprinting for content-based video identification. IEEE Trans Circ Syst Video Tech, 2008, 18: 983–988
Coskun B, Sankur B, Memon N. Spatio-temporal transform based video hashing. IEEE Trans Multimedia, 2006, 8: 1190–1208
Liu L, Peng D, Li X. A security video watermarking scheme for broadcast monitoring. In: Proceedings of IWSDA’07, Chengdu, China, 2007. 109–113
Zhang W, Kong X, You X. Secure and robust perceptual hashing. J South-East Univ (Nat Sci Ed), 2007, 37: 188–192
Swaminathan A, Mao Y, Wu M. Image hashing resilient to geometric and filtering operations. In: IEEE Workshop on Multimedia Signal Processing, Siena, Italy, 2004
He Y. Study of robust and secure digital image signature. Master Thesis. Guangzhou: Sun Yat-sen University, 2007
Swaminathan A, Mao Y, Wu M. Robust and secure image hashing. IEEE Trans Inf Forens Secur, 2006, 1: 215–230
Xiang S J, Kim H J, Huang J W. Histogram-based image hashing scheme robust against geometric deformations. In: Proceeding of the 9th ACM Multimedia and Security Workshop, Dallas, Texas, USA, 2007. 121–128
Qin C, Wang S, Zhang X. Image hashing based on human visual system. J Image Graph, 2006, 11: 1678–1681
Xiang S J, Kim H J, Huang J W. Invariant image watermarking based on statistical features in the low-frequency domain. IEEE Trans Circ Syst Video Tech, 2008, 18: 777–790
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Xiang, S., Yang, J. & Huang, J. Perceptual video hashing robust against geometric distortions. Sci. China Inf. Sci. 55, 1520–1527 (2012). https://doi.org/10.1007/s11432-011-4450-1
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DOI: https://doi.org/10.1007/s11432-011-4450-1