A framework for identifying shifted double JPEG compression artifacts with application to non-intrusive digital image forensics

Non-intrusive digital image forensics (NIDIF) is a novel approach to authenticate the trustworthiness of digital images. It works by exploring varieties of intrinsic characteristics involved in the digital imaging, editing, storing processes as discriminative features to reveal the subtle traces left by a malicious fraudster. The NIDIF for the lossy JPEG image format is of special importance for its pervasive application. In this paper, we propose an NIDIF framework for the JPEG images. The framework involves two complementary identification methods for exposing shifted double JPEG (SD-JPEG) compression artifacts, including an improved ICA-based method and a First Digits Histogram based method. They are designed to treat the detectable conditions and a few special undetectable conditions separately. Detailed theoretical justifications are provided to reveal the relationship between the detectability of the artifacts and some intrinsic statistical characteristics of natural image signal. The extensive experimental results have shown the effectiveness of the proposed methods. Furthermore, some case studies are also given to demonstrate how to reveal certain types of image manipulations, such as cropping, splicing, or both, with our framework.

[1]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[2]  Mo Chen,et al.  Imaging Sensor Noise as Digital X-Ray for Revealing Forgeries , 2007, Information Hiding.

[3]  Jiwu Huang,et al.  A convolutive mixing model for shifted double JPEG compression with application to passive image authentication , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Hany Farid,et al.  Exposing digital forgeries through chromatic aberration , 2006, MM&Sec '06.

[5]  Hany Farid,et al.  Detecting Photographic Composites of People , 2008, IWDW.

[6]  Bin Li,et al.  Detecting doubly compressed JPEG images by using Mode Based First Digit Features , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[7]  Yun Q. Shi,et al.  A natural image model approach to splicing detection , 2007, MM&Sec.

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

[9]  Hany Farid,et al.  Exposing digital forgeries from 3-D lighting environments , 2010, 2010 IEEE International Workshop on Information Forensics and Security.

[10]  Wei Su,et al.  A generalized Benford's law for JPEG coefficients and its applications in image forensics , 2007, Electronic Imaging.

[11]  Jan Lukás,et al.  Estimation of Primary Quantization Matrix in Double Compressed JPEG Images , 2003 .

[12]  Hany Farid,et al.  Detecting Digital Forgeries Using Bispectral Analysis , 1999 .

[13]  Hany Farid,et al.  Digital Image Authentication From JPEG Headers , 2011, IEEE Transactions on Information Forensics and Security.

[14]  Jiwu Huang,et al.  A Novel Method for Detecting Cropped and Recompressed Image Block , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[15]  Hany Farid,et al.  Exposing Digital Forgeries in Complex Lighting Environments , 2007, IEEE Transactions on Information Forensics and Security.

[16]  H. Farid,et al.  Image forgery detection , 2009, IEEE Signal Processing Magazine.

[17]  Jiwu Huang,et al.  Identifying shifted double JPEG compression artifacts for non-intrusive digital image forensics , 2012, CVM'12.

[18]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

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

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

[22]  Ricardo L. de Queiroz,et al.  Identification of bitmap compression history: JPEG detection and quantizer estimation , 2003, IEEE Trans. Image Process..

[23]  James F. O'Brien,et al.  Exposing photo manipulation with inconsistent reflections , 2012, TOGS.

[24]  Junfeng He,et al.  Detecting Doctored JPEG Images Via DCT Coefficient Analysis , 2006, ECCV.

[25]  Shih-Fu Chang,et al.  Using Geometry Invariants for Camera Response Function Estimation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Hany Farid,et al.  Digital image authentication from thumbnails , 2010, Electronic Imaging.

[27]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

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