Source identification of camera phones using SVD

We present a novel method to extract the sensor pattern noise (SPN) of digital images and associate them with their respective source camera phones. The method first estimates the photo response non-uniformity (PRNU) of each image by means of its energy level and then converts it to an additive noise to facilitate extraction using Singular Value Decomposition (SVD). The latter is a spectral decomposition technique that separates the PRNU from the signal subspace. The camera reference signatures of the individual cameras are computed from a sample of their respective images and compared with a mixture of image signatures from a set of known camera devices. Our studies show that it is possible to determine the source device of digital images from camera phones using such method of signature extraction, with encouraging results.

[1]  Ahmad Ryad Soobhany Image source identification and characterisation for forensic analysis , 2013 .

[2]  H. Andrews,et al.  Singular value decompositions and digital image processing , 1976 .

[3]  A. El Gamal,et al.  CMOS image sensors , 2005, IEEE Circuits and Devices Magazine.

[4]  Cor J. Veenman,et al.  Source Camera Identification for Low Resolution Heavily Compressed Images , 2008, 2008 International Conference on Computational Sciences and Its Applications.

[5]  K. P. Lam,et al.  On the Performance of Li's Unsupervised Image Classifier and the Optimal Cropping Position of Images for Forensic Investigations , 2011, Int. J. Digit. Crime Forensics.

[6]  Cleve B. Moler,et al.  10. Eigenvalues and Singular Values , 2004 .

[7]  Heung-Yeung Shum,et al.  Radiometric calibration from a single image , 2004, CVPR 2004.

[8]  Hany Farid,et al.  Digital Image Ballistics from JPEG Quantization , 2006 .

[9]  Mo Chen,et al.  Determining Image Origin and Integrity Using Sensor Noise , 2008, IEEE Transactions on Information Forensics and Security.

[10]  Miroslav Goljan,et al.  Digital camera identification from sensor pattern noise , 2006, IEEE Transactions on Information Forensics and Security.

[11]  K. Unsworth,et al.  A model for measurement of noise in CCD digital-video cameras , 2008 .

[12]  Toby Berger,et al.  Rate distortion theory : a mathematical basis for data compression , 1971 .

[13]  J. Fridrich,et al.  Digital image forensics , 2009, IEEE Signal Processing Magazine.

[14]  Matthew Sorell Conditions for effective detection and identification of primary quantization of re-quantized JPEG images , 2008 .

[15]  Ismail Avcibas,et al.  Source cell phone camera identification based on singular value decomposition , 2009, 2009 First IEEE International Workshop on Information Forensics and Security (WIFS).

[16]  Edmund Y. Lam,et al.  Source camera identification using footprints from lens aberration , 2006, Electronic Imaging.

[17]  Min Wu,et al.  Nonintrusive component forensics of visual sensors using output images , 2007, IEEE Transactions on Information Forensics and Security.

[18]  Rainer Böhme,et al.  Can we trust digital image forensics? , 2007, ACM Multimedia.

[19]  Fawwaz T. Ulaby,et al.  Statistical properties of logarithmically transformed speckle , 2002, IEEE Trans. Geosci. Remote. Sens..

[20]  Chang-Tsun Li Source camera identification using enhanced sensor pattern noise , 2010, IEEE Trans. Inf. Forensics Secur..

[21]  Bülent Sankur,et al.  Blind Identification of Source Cell-Phone Model , 2008, IEEE Transactions on Information Forensics and Security.