TY - GEN
T1 - Detecting doctored images using camera response normality and consistency
AU - Lint, Zhouchen
AU - Wang, Rongrong
AU - Tang, Xiaoou
AU - Shum, Heung Yeung
PY - 2005
Y1 - 2005
N2 - The advance in image/video editing techniques has facilitated people in synthesizing realistic imageshideos that may hard to be distinguished from real ones by visual examination. This poses a problem: how to differentiate real imageshideos from doctored ones? This is a serious problem because some legal issues may occur if there is no reliable way for doctored image/video detection when human inspection fails. Digital watermarking cannot solve this problem completely, We propose an approach that computes the response functions the camera by selecting appropriate patches in different ways. An image may be doctored if the response functions are abnormal or inconsistent to each other. The normality of the response functions is classified by a trained support vector machine (SVM). Experiments show that our method is effective for high-contrast images with many textureless edges.
AB - The advance in image/video editing techniques has facilitated people in synthesizing realistic imageshideos that may hard to be distinguished from real ones by visual examination. This poses a problem: how to differentiate real imageshideos from doctored ones? This is a serious problem because some legal issues may occur if there is no reliable way for doctored image/video detection when human inspection fails. Digital watermarking cannot solve this problem completely, We propose an approach that computes the response functions the camera by selecting appropriate patches in different ways. An image may be doctored if the response functions are abnormal or inconsistent to each other. The normality of the response functions is classified by a trained support vector machine (SVM). Experiments show that our method is effective for high-contrast images with many textureless edges.
UR - https://openalex.org/W2110598603
UR - https://www.scopus.com/pages/publications/33745117985
U2 - 10.1109/CVPR.2005.125
DO - 10.1109/CVPR.2005.125
M3 - Conference Paper published in a book
AN - SCOPUS:33745117985
SN - 0769523722
SN - 9780769523729
T3 - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
SP - 1087
EP - 1092
BT - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
PB - IEEE Computer Society
T2 - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Y2 - 20 June 2005 through 25 June 2005
ER -