CS 68191 Masters Seminar / CS 89191 Doctoral Seminar
Spring 2007



Doctoral Student Presentation

Digital Image Forensics - Detecting Tampered Images

Wei-Hung (Wayne) Cheng



Authentic digital image is a challenging area for image processing research. In the past, researchers used embedding watermarks to tackle digital image tampering; the verification point for this method is extracted watermarks used to claim authenticity or tampered. In practice, most of the digital images are created without watermarks so this method failed [1]. In one of the recent approaches, images can be analyzed by the inconsistency of image quality such as lighting or image source. A technique has developed for spliced image diction by object lighting inconsistency [2]. For example, observing the abnormality in the camera response function, detecting whether two images came from the same camera, and using pattern noise correlation to find the camera source of an image [3, 4, and 5] can be useful. Another approach is based on the statistical view point. This method involves modeling statistical properties with extracted visual features from natural images so that it could be used to differentiate spliced from natural images [6]. For example, used bi-coherence with other features, used wavelet features, and used geometric features can be used for modeling statistical properties of natural images [7, 1] and used to differentiate the spliced digital images.

References

[1] T.-T. Ng, S,-F. Chang, J. Hsu, L. Xie, and M.-P. Tsui, "Physics-motivated features for distinguishing photographic images and computer graphics," in ACM Multimedia, 2005.

[2] M.K. Johnson and H.Farid, "Exposing digital forgeries by detecting inconsistencies in Lighting," in ACM Multimedia and Security Workshop, 2005.

[3] Z. Lin, R. Wang, X. Tang and H.-Y. Shum, "Detecting doctored images using camera response normality and consistency." In CVPR, 2005, pp. 1087-1092.

[4] M.Kharrazi, H. T. Sencar, and N. D. Memon. "Blind source camera identification.," in ICIP, 2004, pp. 709-712.

[5] J. Luks, J. Fridrich, and M. Goljan, "Determining digital image orgin using sensor imperfection," in SPIE, 2005, vol. 5685, pp. 249-260.

[6] T.-T. Ng, S.-F. Chang, and Q. Sun, "Blind detection of photomontage using higher order statistics," in ISCAS, 2004.

[7] H.Farid and S. Lyn, "Higher-order wavelet statistics and their application to digital forensics," in IEEE Workshop on Statistical analysis in Computer Vision, 2003.