Classification of digital camera-models based on demosaicing artifacts

We utilize traces of demosaicing operation in digital cameras to identify the source camera-model of a digital image. To identify demosaicing artifacts associated with different camera-models, we employ two methods and define a set of image characteristics which are used as features in designing classifiers that distinguish between digital camera-models. The first method tries to estimate demosaicing parameters assuming linear model while the second one extracts periodicity features to detect simple forms of demosaicing. To determine the reliability of the designated image features in differentiating the source camera-model, we consider both images taken under similar settings at fixed sceneries and images taken under independent conditions. In order to show how to use these methods as a forensics tool, we consider several scenarios where we try to (i) determine which camera-model was used to capture a given image among three, four, and five camera-models, (ii) decide whether or not a given image was taken by a particular camera-model among very large number of camera-models (in the order of hundreds), and (iii) more reliably identify the individual camera, that captured a given image, by incorporating demosaicing artifacts with noise characteristics of the imaging sensor of the camera.

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

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

[3]  Sevinc Bayram,et al.  IMPROVEMENTS ON SOURCE CAMERA-MODEL IDENTIFICATION BASED ON CFA INTERPOLATION , 2005 .

[4]  Jan Lukás,et al.  Determining digital image origin using sensor imperfections , 2005, IS&T/SPIE Electronic Imaging.

[5]  Mo Chen,et al.  Source digital camcorder identification using sensor photo response non-uniformity , 2007, Electronic Imaging.

[6]  T. Moon The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[7]  Shih-Fu Chang,et al.  Physics-motivated features for distinguishing photographic images and computer graphics , 2005, ACM Multimedia.

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

[9]  Nasir D. Memon,et al.  Source camera identification based on CFA interpolation , 2005, IEEE International Conference on Image Processing 2005.

[10]  Tom E. Bishop,et al.  Blind Image Restoration Using a Block-Stationary Signal Model , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

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

[12]  Kenji Kurosawa,et al.  CCD fingerprint method-identification of a video camera from videotaped images , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[13]  Jessica Fridrich,et al.  Detection of Copy-Move Forgery in Digital Images , 2004 .

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

[15]  B. Sankur,et al.  Source Cell-phone Identification , 2006, 2006 IEEE 14th Signal Processing and Communications Applications.

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

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

[18]  Min Wu,et al.  Non-Intrusive Forensic Analysis of Visual Sensors Using Output Images , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[19]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005, IEEE Transactions on Signal Processing.

[20]  Min Wu,et al.  Image Tampering Identification using Blind Deconvolution , 2006, 2006 International Conference on Image Processing.

[21]  Shih-Fu Chang,et al.  Passive-blind Image Forensics , 2006 .

[22]  Nasir D. Memon,et al.  Blind source camera identification , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[23]  Charles Elkan,et al.  Expectation Maximization Algorithm , 2010, Encyclopedia of Machine Learning.

[24]  Siwei Lyu,et al.  Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines , 2002, Information Hiding.

[25]  Ying Wang,et al.  On Discrimination between Photorealistic and Photographic Images , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

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

[29]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005 .

[30]  Nasir D. Memon,et al.  Image manipulation detection , 2006, J. Electronic Imaging.

[31]  Mo Chen,et al.  Digital imaging sensor identification (further study) , 2007, Electronic Imaging.

[32]  Andrew C. Gallagher Detection of linear and cubic interpolation in JPEG compressed images , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

[33]  Siwei Lyu,et al.  Higher-order Wavelet Statistics and their Application to Digital Forensics , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[34]  Nasir D. Memon,et al.  Improvements on Sensor Noise Based Source Camera Identification , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[35]  Josef Kittler,et al.  Floating search methods for feature selection with nonmonotonic criterion functions , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[36]  Hany Farid,et al.  Exposing digital forgeries by detecting inconsistencies in lighting , 2005, MM&Sec '05.

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

[38]  Husrev T. Sencar,et al.  Overview of State-of-the-Art in Digital Image Forensics , 2007 .

[39]  Nasir D. Memon,et al.  New Features to Identify Computer Generated Images , 2007, 2007 IEEE International Conference on Image Processing.

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