Physics-motivated features for distinguishing photographic images and computer graphics

The increasing photorealism for computer graphics has made computer graphics a convincing form of image forgery. Therefore, classifying photographic images and photorealistic computer graphics has become an important problem for image forgery detection. In this paper, we propose a new geometry-based image model, motivated by the physical image generation process, to tackle the above-mentioned problem. The proposed model reveals certain physical differences between the two image categories, such as the gamma correction in photographic images and the sharp structures in computer graphics. For the problem of image forgery detection, we propose two levels of image authenticity definition, i.e., imaging-process authenticity and scene authenticity, and analyze our technique against these definitions. Such definition is important for making the concept of image authenticity computable. Apart from offering physical insights, our technique with a classification accuracy of 83.5% outperforms those in the prior work, i.e., wavelet features at 80.3% and cartoon features at 71.0%. We also consider a recapturing attack scenario and propose a counter-attack measure. In addition, we constructed a publicly available benchmark dataset with images of diverse content and computer graphics of high photorealism.

[1]  T. C. Huang,et al.  Engineering Mechanics, Volume 2: Dynamics , 1967 .

[2]  Thomas S. Huang,et al.  Image processing , 1971 .

[3]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[4]  Alex Pentland,et al.  On describing complex surface shapes , 1986, Image Vis. Comput..

[5]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[6]  Shih-Fu Chang,et al.  Visually Searching the Web for Content , 1997, IEEE Multim..

[7]  Ron Kimmel,et al.  A general framework for low level vision , 1998, IEEE Trans. Image Process..

[8]  Takeo Kanade,et al.  Statistical Calibration of the CCD Imaging Process , 2001, ICCV.

[9]  Brendan J. Frey,et al.  Unsupervised image translation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Arjen P. de Vries,et al.  Detecting cartoons: a case study in automatic video-genre classification , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[11]  Eero P. Simoncelli,et al.  On Advances in Statistical Modeling of Natural Images , 2004, Journal of Mathematical Imaging and Vision.

[12]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Kim Steenstrup Pedersen,et al.  The Nonlinear Statistics of High-Contrast Patches in Natural Images , 2003, International Journal of Computer Vision.

[14]  Shih-Fu Chang,et al.  Columbia Photographic Images and Photorealistic Computer Graphics Dataset , 2005 .

[15]  Siwei Lyu,et al.  How realistic is photorealistic? , 2005, IEEE Transactions on Signal Processing.

[16]  Dipl.-Ing,et al.  Real-time Rendering , 2022 .