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Issue No.03 - March (2008 vol.30)
pp: 532-534
ABSTRACT
Previous studies have shown how to "fingerprint" a digital camera given a set of images known to come from the camera. A clustering technique is proposed to construct such fingerprints from a mixed set of images, enabling identification of each image's source camera without any prior knowledge of source.
INDEX TERMS
clustering algorithms, forensics, image processing, pattern recognition, machine learning
CITATION
Greg J. Bloy, "Blind Camera Fingerprinting and Image Clustering", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 3, pp. 532-534, March 2008, doi:10.1109/TPAMI.2007.1183
REFERENCES
[1] Z.J. Geradts et al., “Methods for Identification of Images Acquired with Digital Cameras,” Proc. SPIE Enabling Technologies for Law Enforcement and Security, vol. 4232, 2001.
[2] J. Lukáš, J. Fridrich, and M. Goljan, “Determining Digital Image Origin Using Sensor Imperfections,” Proc. SPIE Electronic Imaging, Image and Video Communication, and Processing, pp. 249-260, Jan. 2005.
[3] J. Lukáš, J. Fridrich, and M. Goljan, “Digital ‘Bullet Scratches’ for Images,” Proc. Int'l Conf. Image Processing, Sept. 2005.
[4] J. Lukáš, J. Fridrich, and M. Goljan, “Determining Digital Image Origin Using Sensor Imperfections,” IEEE Trans. Information Forensics and Security, vol. 1, no. 2, pp. 205-214, June 2006.
[5] S. Kundu, “Gravitational Clustering: A New Approach Based on the Spatial Distribution of the Points,” Pattern Recognition, vol. 32, no. 7, pp. 1149-1160, 1999.
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