Using the local information of image to identify the source camera

In this paper we introduce a new method for source identification in digital image forensics. The proposed method uses local information of the inherent pattern of the camera, as a signature of the camera for source identification. Here the sensor pattern noise is used as the unique identification property of the camera. However due to content dependency of the denoising algorithms that are used to extract the noise pattern, the different regions of the image do not have the same information about the camera signature. Hence in our algorithm, at first the best regions of the image according to their local information are selected to extract the noise pattern. This step is done by fuzzy-based classification on the overlapped blocks of the image. In the next step the noise pattern of these regions are extracted and then, we evaluate the correlation between the image pattern and camera pattern. Finally the source camera is determined according its correlation. The experimental results compared to similar works show an increase in the detection rate of source identification, while computational complexity is reduced; this affirms the efficiency and performance of the proposed theory.

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

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

[3]  Kannan Ramchandran,et al.  Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

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

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

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

[7]  Jing Dong,et al.  Run-Length and Edge Statistics Based Approach for Image Splicing Detection , 2009, IWDW.

[8]  Soodeh Bakhshandeh,et al.  Blind Image Steganalysis Based on Local Information and Human Visual System , 2009, FGIT-SIP.

[9]  Jan P. Allebach,et al.  Forensic classification of imaging sensor types , 2007, Electronic Imaging.

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

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

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

[13]  Dong-Hwan Har,et al.  Detection of digital forgeries using an image interpolation from digital images , 2008, 2008 IEEE International Symposium on Consumer Electronics.

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