Evaluation of Residual-Based Local Features for Camera Model Identification

Camera model identification is of interest for many applications. In-camera processes, specific of each model, leave traces that can be captured by features designed ad hoc, and used for reliable classification. In this work we investigate on the use of blind features based on the analysis of image residuals. In particular, features are extracted locally based on co-occurrence matrices of selected neighbors and then used to train an SVM classifier. Experiments on the well-known Dresden database show this approach to provide state-of-the-art performances.

[1]  Florent Retraint,et al.  Camera model identification based on DCT coefficient statistics , 2015, Digit. Signal Process..

[2]  Davide Cozzolino,et al.  A feature-based approach for image tampering detection and localization , 2014, 2014 IEEE International Workshop on Information Forensics and Security (WIFS).

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

[4]  Alex ChiChung Kot,et al.  Accurate Detection of Demosaicing Regularity for Digital Image Forensics , 2009, IEEE Transactions on Information Forensics and Security.

[5]  Florent Retraint,et al.  Camera Model Identification Based on the Heteroscedastic Noise Model , 2014, IEEE Transactions on Image Processing.

[6]  Kai San Choi,et al.  Source Camera Identification by JPEG Compression Statistics for Image Forensics , 2006, TENCON 2006 - 2006 IEEE Region 10 Conference.

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

[8]  Yun Q. Shi,et al.  A Markov Process Based Approach to Effective Attacking JPEG Steganography , 2006, Information Hiding.

[9]  Yun Q. Shi,et al.  Camera Model Identification Using Local Binary Patterns , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[10]  Jessica J. Fridrich,et al.  On detection of median filtering in digital images , 2010, Electronic Imaging.

[11]  Thomas Gloe,et al.  Feature-Based Forensic Camera Model Identification , 2012, Trans. Data Hiding Multim. Secur..

[12]  Luisa Verdoliva,et al.  On the influence of denoising in PRNU based forgery detection , 2010, MiFor '10.

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

[14]  Luisa Verdoliva,et al.  An Investigation of Local Descriptors for Biometric Spoofing Detection , 2015, IEEE Transactions on Information Forensics and Security.

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

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

[17]  Shujun Li,et al.  Forensic Camera Model Identification , 2015 .

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

[19]  Nasir D. Memon,et al.  Identifying Digital Cameras Using CFA Interpolation , 2006, IFIP Int. Conf. Digital Forensics.

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

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

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

[23]  Siwei Lyu,et al.  Steganalysis using higher-order image statistics , 2006, IEEE Transactions on Information Forensics and Security.

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

[25]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

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

[27]  Shang Gao,et al.  Camera-Model Identification Using Markovian Transition Probability Matrix , 2009, IWDW.