Effective Source Camera Identification based on MSEPLL Denoising Applied to Small Image Patches

Sensor Pattern Noise (SPN) has proven to be an effective fingerprint for source camera identification, while its estimation accuracy heavily relies on denoising algorithm. In this paper, an effective source camera identification scheme based on Multi-Scale Expected Patch Log Likelihood (MSEPLL) denoising algorithm is proposed, firstly. With enhanced prior modeling across multiple scales, MSEPLL can accurately restore the original image. As a consequence, estimated SPN is less influenced by image content. Secondly, the source camera identification problem is formulated by hypothesis testing, where normalized correlation coefficient is adopted for SPN detection. Finally, the effectiveness of the proposed method is verified by abundant experiments in terms of identification accuracy as well as receiver operating characteristic. Performance improvement is more prominent for small image patches, which is more conducive to real forensics applications.

[1]  Donald Geman,et al.  Nonlinear image recovery with half-quadratic regularization , 1995, IEEE Trans. Image Process..

[2]  Enrico Magli,et al.  Compressed Fingerprint Matching and Camera Identification via Random Projections , 2015, IEEE Transactions on Information Forensics and Security.

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

[4]  Wan-Chi Siu,et al.  A confidence map and pixel-based weighted correlation for PRNU-based camera identification , 2013, Digit. Investig..

[5]  Xufeng Lin,et al.  Preprocessing Reference Sensor Pattern Noise via Spectrum Equalization , 2016, IEEE Transactions on Information Forensics and Security.

[6]  Fei Peng,et al.  Identifying source camera using guided image estimation and block weighted average , 2017, J. Vis. Commun. Image Represent..

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

[8]  Luisa Verdoliva,et al.  Blind PRNU-Based Image Clustering for Source Identification , 2017, IEEE Transactions on Information Forensics and Security.

[9]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  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).

[11]  Chang-Tsun Li,et al.  Source Camera Identification Using Enhanced Sensor Pattern Noise , 2009, IEEE Transactions on Information Forensics and Security.

[12]  Xiangui Kang,et al.  A context-adaptive SPN predictor for trustworthy source camera identification , 2014, EURASIP J. Image Video Process..

[13]  Michael Elad,et al.  Multi-Scale Patch-Based Image Restoration , 2016, IEEE Transactions on Image Processing.

[14]  King-Sun Fu,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Publication Information , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Ming Zhang,et al.  e-PRNU: Encrypted Domain PRNU-Based Camera Attribution for Preserving Privacy , 2021, IEEE Transactions on Dependable and Secure Computing.

[16]  Fouad Khelifi,et al.  On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation , 2017, IEEE Transactions on Information Forensics and Security.

[17]  Xiangui Kang,et al.  Fast Source Camera Identification Using Content Adaptive Guided Image Filter , 2016, Journal of forensic sciences.

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