Photographic composite detection using circles

Abstract

In this work, we propose several new methods for detecting photographic composites using circles. In particular, we focus on three kinds of scenes: (1) two coplanar circles with the same radius; (2) a single circle with its discriminable center; (3) a single circle with geometric constraints for camera calibration. For two circles’ situation, we first estimate the focal length based on the equality of the sizes of two coplanar circles, and then estimate the normal vector of the world circle plane. Inconsistencies in the angles among the normal vectors (Each circle determines a normal vector) are used as evidence of tampering. On the other hand, for the single circle case, we warp the circle to make metric measurement. To demonstrate the effectiveness of the approach, we present results for synthetic and visually plausible composite images.

Keywords

digital forensics / two circles / a single circle / geometric constraints

References

[1] Huang P, Tang Z M, Chen C-K, Cheng X T. Nearest-neighbor classifier motivated marginal discriminant projections for face recognition. Frontiers of Computer Science in China, 2011, 5(4): 419-428

[2] Zhao F, Jiao L C, Liu H Q. Fuzzy c-means clustering with non local spatial information for noisy image segmentation. Frontiers of Computer Science in China, 2011, 5(1): 45-56

[3] Chen Z Y, Fan W, Xiong Z, Zhang P G, Luo L X. Visual data security and management for smart cities. Frontiers of Computer Science in China, 2010, 4(3): 386-393

[4] Farid H. A survey of image forgery detection. IEEE Signal Processing Magazine, 2009, 26(2): 16-25

[5] Dirik A E, Sencar H T, Memon N. Digital single lens reflex camera identification from traces of sensor dust. IEEE Transactions on Information Forensics and Security, 2008, 3(3): 539-552

[6] Swaminathan A, Wu M, Liu K J R. Digital image forensics via intrinsic fingerprints. IEEE Transactions on Information Forensics and Security, 2008, 3(1): 101-117

[7] Cox I J, Miller M L, Bloom J A. Digital watermarking. Morgan Kaufmann Publishers, 2002

[8] Liu H, Rao J, Yao X. Feature based watermarking scheme for image authentication. In: Proceedings of IEEE International Conference on Multimedia & Expo. 2008, 229-232

[9] Fridrich J, Soukal D, Lukáš J. Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop, Cleveland, OH. 2003

[10] Ngand T T, Chang S F. A model for image splicing. In: Proceedings of IEEE International Conference on Image Processing. 2004, 1169-1172

[11] Popescu A C, Farid H. Exposing digital forgeries by detecting traces of re-sampling. IEEE Signal Processing Magazine, 2005, 53(2): 758-767

[12] Bayram S, Avcibas I, Sankur B, Memon N. Image manipulation detection with binary similarity measures. In: Proceedings of European Signal Processing Conference. 2005

[13] Luo W, Huang J, Qiu G. JPEG error analysis and its applications to digital image forensics. IEEE Transactions on Information Forensics and Security, 2010, 5(3): 480-491

[14] Farid H. Digital image ballistics from JPEG quantization: a follow up study. Department of Computer Science, Dartmouth College, 2008

[15] He J, Lin Z, Wang L, Tang X. Detecting doctored jpeg images via DCT coefficient analysis. In: Proceedings of European Conference on Computer Vision. 2006, 423-435

[16] Farid H. Exposing digital forgeries from JPEG ghosts. IEEE Transactions on Information Forensics and Security, 2009, 1(4): 154-160

[17] Hsu Y F, Chang S F. Image splicing detection using camera response function consistency and automatic segmentation. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2007, 28-31

[18] Lukáš J, Fridrich J, Goljan M. Detecting digital image forgeries using sensor pattern noise. Society of Photo-Optical Instrumentation Engineers Conference Series, 2006, 6072: 362-372

[19] Popescu A C, Farid H. Exposing digital forgeries in color filter array interpolated images. IEEE Transactions on Signal Processing, 2005, 53(10): 3948-3959

[20] Johnson M K, Farid H. Exposing digital forgeries through chromatic aberration. In: Proceedings of ACM Multimedia and Security Workshop, Geneva, Switzerland. 2006

[21] Zhang W, Cao X, Zhang J, Zhu J, Wang P. Detecting photographic composites using shadows. In: Proceedings of IEEE International Conference on Multimedia & Expo. 2009, 1042-1045

[22] Liu Q G, Cao X C, Deng C, Guo X J. Identifying image composites through shadow matte consistency. IEEE Transactions on Information Forensics and Security, 2010, 6(3-2): 1111-1122

[23] Johnson M K, Farid H. Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of ACM Multimedia and Security Workshop. 2005, 1-9

[24] Criminisi A, Reid I, Zisserman A. Single view metrology. International Journal of Computer Vision, 2000, 40(2): 123-148

[25] Johnson M K, Farid H. Detecting photographic composites of people. In: Proceedings of the 6th International Workshop on Digital Watermarking. 2007

[26] Kee E, Farid H. Detecting photographic composites of famous people. Department of Computer Science, Dartmouth College, 2009

[27] Wang W, Farid H. Detecting re-projected video. In: Proceedings of the 10th International Workshop on Information Hiding. 2008

[28] Johnson M K, Farid H. Metric measurements on a plane from a single image. Department of Computer Science, Dartmouth College, 2006

[29] Conotter V, Boato G, Farid H. Detecting photo manipulation on signs and bill boards. In: Proceedings of the 17th IEEE International Conference on Image Processing. 2010, 1741-1744

[30] Zhang W, Cao X, Feng Z, Zhang J, Wang P. Detecting photographic composites using two-view geometrical constraints. In: Proceedings of IEEE International Conference on Multimedia & Expo. 2009, 1078-1081

[31] Zhang W, Cao X, Qu Y, Hou Y, Zhao H, Zhang C. Detecting and extracting the photo composites using planar homography and graph cut. IEEE Transactions on Information Forensics and Security, 2010, 5(2): 544-555

[32] Deng Z P, Jia K B, Chan Y-L, Fu C-H, Siu W-C. Fast motion and disparity estimation for multiview video coding. Frontiers of Computer Science in China, 2010, 4(4): 571-579

[33] Wu H, Chen Q, Wada T. Visual direction estimation from a monocular image. IEICE Transactions on Information and Systems, 2005, E88- D(10): 2277-2285

[34] Hartley R, Zisserman A. Multiple View Geometry in Comput7er Vision. Cambridge University Press, 2004

[35] Zhang Y, Liu Y. The projective equation of a circle and its application in camera calibration. In: Proceedings of the 19th International Conference on Pattern Recognition. 2008, 1-4

[36] Wang G, Wu J, Ji Z. Single view based pose estimation from circle or parallel lines. Pattern Recognition Letters, 2008, 29(7): 977-985

[37] Fitzgibbon A, Pilu M, Fisher R B. Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis and Machine Intelli gence, 1999, 21(5): 476-480

[38] Zhang Z. A flexible new technique for camera calibration. IEEE Transactions Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334

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