TY - GEN
T1 - Physics-motivated features for distinguishing photographic images and computer graphics
AU - Ng, Tian Tsong
AU - Chang, Shih Fu
AU - Hsu, Jessie
AU - Xie, Lexing
AU - Tsui, Mao Pei
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Computer graphics
KW - Differential geometry
KW - Fractal
KW - Image forensics
KW - Natural image statistics
KW - Steganalysis
UR - http://www.scopus.com/inward/record.url?scp=84883130375&partnerID=8YFLogxK
U2 - 10.1145/1101149.1101192
DO - 10.1145/1101149.1101192
M3 - Conference contribution
SN - 1595930442
SN - 9781595930446
T3 - Proceedings of the 13th ACM International Conference on Multimedia, MM 2005
SP - 239
EP - 248
BT - Proceedings of the 13th ACM International Conference on Multimedia, MM 2005
T2 - 13th ACM International Conference on Multimedia, MM 2005
Y2 - 6 November 2005 through 11 November 2005
ER -