Differentiating Between Images Using Wavelet-Based Transforms: A Comparative Study

We propose statistical image models for wavelet-based transforms, investigate their use, and compare their relative merits within the context of digital image forensics. We consider the problems of 1) differentiating computer graphics images from photographic images, 2) source camera and source scanner identification, and 3) source artist identification from digital painting samples. The features obtained from ridgelet and contourlet transform-based image models almost always perform better than the features obtained from wavelet-based image models for the problems at hand. We outline properties of efficient image representation, relate these properties to wavelet-based transforms, and discuss the experimental results in relation to the model properties.

[1]  Mark J. T. Smith,et al.  A filter bank for the directional decomposition of images: theory and design , 1992, IEEE Trans. Signal Process..

[2]  Minh N. Do,et al.  The finite ridgelet transform for image representation , 2003, IEEE Trans. Image Process..

[3]  Shih-Fu Chang,et al.  Physics-motivated features for distinguishing photographic images and computer graphics , 2005, ACM Multimedia.

[4]  Siwei Lyu,et al.  How realistic is photorealistic , 2005 .

[5]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[6]  Jia Li,et al.  Image processing for artist identification , 2008, IEEE Signal Processing Magazine.

[7]  Eero P. Simoncelli,et al.  Image compression via joint statistical characterization in the wavelet domain , 1999, IEEE Trans. Image Process..

[8]  E. Candès,et al.  Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges , 2000 .

[9]  M. Do.  Directional multiresolution image representations , 2002 .

[10]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[11]  Edward J. Delp,et al.  Scanner Identification Using Feature-Based Processing and Analysis , 2009, IEEE Transactions on Information Forensics and Security.

[12]  Min Wu,et al.  Digital forensics [From the Guest Editors] , 2009 .

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

[14]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

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

[16]  E. Candès,et al.  Ridgelets: a key to higher-dimensional intermittency? , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[17]  Aleksandra Pizurica,et al.  Modeling curvelet domain inter-band image statistics with application to spatially adaptive image denoising. , 2006 .

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

[19]  E. Candès,et al.  New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities , 2004 .

[20]  Babak Mahdian,et al.  A bibliography on blind methods for identifying image forgery , 2010, Signal Process. Image Commun..

[21]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.