Commenced in January 2007
Paper Count: 31532
Evaluation of Sensor Pattern Noise Estimators for Source Camera Identification
Abstract:This paper presents a comprehensive survey of recent source camera identification (SCI) systems. Then, the performance of various sensor pattern noise (SPN) estimators was experimentally assessed, under common photo response non-uniformity (PRNU) frameworks. The experiments used 1350 natural and 900 flat-field images, captured by 18 individual cameras. 12 different experiments, grouped into three sets, were conducted. The results were analyzed using the receiver operator characteristic (ROC) curves. The experimental results demonstrated that combining the basic SPN estimator with a wavelet-based filtering scheme provides promising results. However, the phase SPN estimator fits better with both patch-based (BM3D) and anisotropic diffusion (AD) filtering schemes.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339918Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 795
 J. Fridrich “Sensor Defects In Digital Image Forensic”, Digital Image Forensics, Vol. 2, No. 2, pp. 179-218, July 2012.
 S. Bayram, H. Sencar, N. Memon, and I. Avcibas, “Source camera identification based on CFA interpolation,” IEEE International Conference on Image Processing., vol. 3, pp. III-69–III-72, Sep. 2005.
 M. Swaminathan, Wu, and K. J. R. Liu, “Nonintrusive component forensics of visual sensors using output images,” IEEE Transactions on Information Forensics and Security, vol. 2, no. 1, pp. 91–106, Mar. 2007.
 M. J. Sorrell, “Digital camera source identification through JPEG quantisation,” Multimedia Forensics and Security, pp. 291–313, 2008.
 E. J. Alles, Z. J. M. H. Geradts, and C. J. Veenman, “Source camera identification for heavily JPEG compressed low resolution still images,”Journal of Forensic Science, vol. 54, no. 3, pp. 628–638, May 2009.
 T. Gloe, S. Pfennig, and M. Kirchner, “Unexpected Artefacts in PRNU Based Camera Identification: A ‘Dresden Image Database’ Case-Study,” in Proc. ACM Workshop Multimedia Security, pp. 109–114, Sep. 2012.
 MK Mihcak, I Kozintsev, K Ramchandran, “Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising.” IEEE international conference on acoustics, speech, and signal processing, vol. 6, pp. 3253–3256, May 1999
 J. Lukas, J. Fridrich, and M. Goljan, “Digital camera identification from sensor pattern noise,” IEEE Transactions on Information Forensics and Security, vol. 1, no. 2, pp. 205–214, Jun. 2006.
 M. Goljan, J. Fridrich “Camera identification from scaled and cropped images,” Proceedings of SPIE electronic imaging, security, forensics, steganography, and watermarking of multimedia contents X, vol 6819, pp OE1-OE13, 2008
 CR. Holt “Two-channel detectors for arbitrary linear channel distortion,” IEEE Trans Acoust Speech Signal Process ASSP-35(3):267–273, 1987
 M. Chen, J. Fridrich, M. Goljan, and J. Lukás, “Determining image origin and integrity using sensor noise,” IEEE Transactions on Information Forensics and Security, vol. 3, no. 1, pp. 74–90, Mar. 2008.
 A. Cooper, “Improved photo response non-uniformity (prnu) based source camera identification,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 2, pp. 260-271, Feb. 2012
 G. Chierchia, S. Parrilli, G. Poggi, C. Sansone, and L. Verdoliva, “On the influence of denoising in PRNU based forgery detection,” Image Processing and Computer Vision-General, pp. 117–122, Oct. 2010
 K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering”, IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2080–2095, Aug. 2007.
 W.V. Houten, Z. Geradts, “Using Anisotropic Diffusion for Efficient Extraction of Sensor Noise in Camera Identification”, Journal of Forensic Sciences., vol. 57, no. 2, pp. 521-527, March 2012.
 P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, July 1990.
 F. Gisolf, A. Malgoezar, T. Baar, Z. Geradts: ‘Improving source camera identification using a simplified total variation based noise removal algorithm’, Digital Investigation, vol. 10, no. 3, pp. 207-214, Oct. 2013.
 T. Gloe and R. Böhme, “The dresden image database for benchmarking digital image forensics,” Journal of Digital Forensic Practice, vol. 3, no. 2–4, pp. 150–159, 2010.
 X. Kang, Y. Li, Z. Qu, and J. Huang, “Enhancing source camera identification performance with a camera reference phase sensor pattern noise,” IEEE Transactions on Information Forensics and Security, vol. 7, no. 2, pp. 393–402, Apr. 2012.