Case studies and further improvements on source camera identification

Actual case examples and further improvements on source camera identification are shown. There are three specific topics in this paper: (a) In order to improve performance of source camera identification, the hybrid identification scheme using both dark current non-uniformity (DCNU) and photo-response non-uniformity (PRNU) is proposed. The experimental results indicated that identification performance would be improved by properly taking advantage of their features; (b) Source camera identification using non-uniform nature of the CCD charge transfer circuit is proposed. The experimental results with twenty CCD modules of the same model showed that individual camera identification for dark images was possible by the proposed method. Furthermore, it was shown that the proposed method had higher discrimination capability than the method using pixel non-uniformity when the number of recorded image was small; (c) The authors have been performed source camera identification in the five actual criminal cases, such as homicide case, and so on. The analytical procedure was a sequential examination of hot pixel coordinates validation followed by the similarity evaluation of sensor noise pattern. The authors could clearly prove that the questioned criminal scenes had been recorded by the questioned cameras in four cases of the five.

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

[2]  Nasir D. Memon,et al.  Efficient Sensor Fingerprint Matching Through Fingerprint Binarization , 2012, IEEE Transactions on Information Forensics and Security.

[3]  Zeno Geradts,et al.  CCD fingerprint method for digital still cameras , 2002, SPIE Defense + Commercial Sensing.

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

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

[6]  Mo Chen,et al.  Sensor noise camera identification: countering counter-forensics , 2010, Electronic Imaging.

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

[8]  Jessica J. Fridrich,et al.  Camera identification from cropped and scaled images , 2008, Electronic Imaging.

[9]  Kenji Kurosawa,et al.  Fundamental study on identification of CMOS cameras , 2003, SPIE Defense + Commercial Sensing.

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

[11]  Mo Chen,et al.  Defending Against Fingerprint-Copy Attack in Sensor-Based Camera Identification , 2011, IEEE Transactions on Information Forensics and Security.

[12]  Jessica J. Fridrich,et al.  Managing a large database of camera fingerprints , 2010, Electronic Imaging.

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

[14]  Jan Lukás,et al.  Camera identification from printed images , 2008, Electronic Imaging.

[15]  Kenji Kurosawa,et al.  An Approach to Individual Video Camera Identification , 2002 .

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

[17]  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).

[18]  Mohan S. Kankanhalli,et al.  Identifying Source Cell Phone using Chromatic Aberration , 2007, 2007 IEEE International Conference on Multimedia and Expo.