Source camera identification using Auto-White Balance approximation

Source camera identification finds many applications in real world. Although many identification methods have been proposed, they work with only a small set of cameras, and are weak at identifying cameras of the same model. Based on the observation that a digital image would not change if the same Auto-White Balance (AWB) algorithm is applied for the second time, this paper proposes to identify the source camera by approximating the AWB algorithm used inside the camera. To the best of our knowledge, this is the first time that a source camera identification method based on AWB has been reported. Experiments show near perfect accuracy in identifying cameras of different brands and models. Besides, proposed method performances quite well in distinguishing among camera devices of the same model, as AWB is done at the end of imaging pipeline, any small differences induced earlier will lead to different types of AWB output. Furthermore, the performance remains stable as the number of cameras grows large.

[1]  Tian-Tsong Ng,et al.  Camera response function signature for digital forensics - Part I: Theory and data selection , 2009, 2009 First IEEE International Workshop on Information Forensics and Security (WIFS).

[2]  E. Land The retinex theory of color vision. , 1977, Scientific American.

[3]  Nasir D. Memon,et al.  Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust , 2008, IEEE Transactions on Information Forensics and Security.

[4]  M. H. Brill,et al.  Necessary and sufficient conditions for Von Kries chromatic adaptation to give color constancy , 1982, Journal of mathematical biology.

[5]  Jan P. Allebach,et al.  Methodology for designing image similarity metrics based on human visual system models , 1997, Electronic Imaging.

[6]  D. L. Macadam Chromatic adaptation. , 1956, Journal of the Optical Society of America.

[7]  Thomas Gloe,et al.  Feature-Based Camera Model Identification Works in Practice , 2009, Information Hiding.

[8]  Brian V. Funt,et al.  A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data , 2002, IEEE Trans. Image Process..

[9]  Miroslav Goljan,et al.  Using sensor pattern noise for camera model identification , 2008, 2008 15th IEEE International Conference on Image Processing.

[10]  R.W. Schafer,et al.  Demosaicking: color filter array interpolation , 2005, IEEE Signal Processing Magazine.

[11]  Edmund Y Lam,et al.  Automatic source camera identification using the intrinsic lens radial distortion. , 2006, Optics express.

[12]  Pan Feng,et al.  A survey of passive technology for digital image forensics , 2007 .

[13]  Husrev T. Sencar,et al.  Source Camera Identification Based on Sensor Dust Characteristics , 2007 .

[14]  Zeno Geradts,et al.  Methods for identification of images acquired with digital cameras , 2001, SPIE Optics East.

[15]  Jan Lukás,et al.  Detecting digital image forgeries using sensor pattern noise , 2006, Electronic Imaging.

[16]  M. S. Drew,et al.  Color constancy - Generalized diagonal transforms suffice , 1994 .

[17]  Jiwu Huang,et al.  A survey of passive technology for digital image forensics , 2007, Frontiers of Computer Science in China.

[18]  G. Buchsbaum A spatial processor model for object colour perception , 1980 .

[19]  Mark D. Fairchild,et al.  Color Appearance Models , 1997, Computer Vision, A Reference Guide.

[20]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[21]  Brian V. Funt,et al.  A comparison of computational color constancy Algorithms. II. Experiments with image data , 2002, IEEE Trans. Image Process..

[22]  Joost van de Weijer,et al.  Author Manuscript, Published in "ieee Transactions on Image Processing Edge-based Color Constancy , 2022 .

[23]  Kevin E. Spaulding,et al.  Color processing in digital cameras , 1998, IEEE Micro.

[24]  Rainer Böhme,et al.  The 'Dresden Image Database' for benchmarking digital image forensics , 2010, SAC '10.

[25]  Michael H. Brill,et al.  Color appearance models , 1998 .

[26]  Mohan S. Kankanhalli,et al.  A Survey on Digital Camera Image Forensic Methods , 2007, 2007 IEEE International Conference on Multimedia and Expo.

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

[28]  Alin C. Popescu,et al.  Exposing digital forgeries in color filter array interpolated images , 2005, IEEE Transactions on Signal Processing.

[29]  R. Schafer,et al.  Demosaicking: Color Filter Array Interpolation in Single-Chip Digital Cameras , 2003 .

[30]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[31]  J. Rafael Sendra,et al.  An Algebraic Approach to Lens Distortion by Line Rectification , 2009, Journal of Mathematical Imaging and Vision.

[32]  Nasir D. Memon,et al.  Steganalysis using image quality metrics , 2003, IEEE Trans. Image Process..

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

[34]  Yizhen Huang,et al.  Image Based Source Camera Identification using Demosaicking , 2006, 2006 IEEE Workshop on Multimedia Signal Processing.

[35]  Graham D. Finlayson,et al.  Shades of Gray and Colour Constancy , 2004, CIC.