Exposing photo manipulation with inconsistent perspective geometry

Manipulated digital image is got interesting in recent years. Digital images can be manipulated more easily with the aid of powerful image editing software. Forensic techniques for authenticating the integrity of digital images and exposing forgeries are urgently needed. A geometric-based forensic technique which exploits the principle of vanishing points is proposed. By means of edge detection and straight lines extraction, intersection points of the projected parallel lines are computed. The normalized mean value (NMV) and normalized standard deviation (NSD) of the distances between the intersection points are used as evidence for image forensics. The proposed method employs basic rules of linear perspective projection, and makes minimal assumption. The only requirement is that the parallel lines are contained in the image. Unlike other forensic techniques which are based on low-level statistics, this method is less sensitive to image operations that do not alter image content, such as image resampling, color manipulation, and lossy compression. This method is demonstrated with images from York Urban database. It shows that the proposed method has a definite advantage at separating authentic and forged images.

[1]  James F. O'Brien,et al.  Exposing photo manipulation with inconsistent reflections , 2012, TOGS.

[2]  Hong Yiguang,et al.  Observability analysis and observer design for finite automata via matrix approach , 2013 .

[3]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Meng,et al.  Detecting Photographic Cropping Based on Vanishing Points , 2013 .

[5]  Jan Lukás,et al.  Estimation of Primary Quantization Matrix in Double Compressed JPEG Images , 2003 .

[6]  Tomás Pevný,et al.  Detection of Double-Compression in JPEG Images for Applications in Steganography , 2008, IEEE Transactions on Information Forensics and Security.

[7]  H. Farid Image Forgery Detection -- A survey , 2009 .

[8]  Pascal Vasseur,et al.  Globally optimal line clustering and vanishing point estimation in Manhattan world , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Mary J. Bravo,et al.  Image forensic analyses that elude the human visual system , 2010, Electronic Imaging.

[10]  Chang-Tsun Li Source camera identification using enhanced sensor pattern noise , 2010, IEEE Trans. Inf. Forensics Secur..

[11]  Anthony Hoogs,et al.  A Minimum Error Vanishing Point Detection Approach for Uncalibrated Monocular Images of Man-Made Environments , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Shih-Fu Chang,et al.  Image Splicing Detection using Camera Response Function Consistency and Automatic Segmentation , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[13]  Derek Nowrouzezahrai,et al.  Learning hatching for pen-and-ink illustration of surfaces , 2012, TOGS.

[14]  Xinpeng Zhang,et al.  Detecting Image Forgery Using Perspective Constraints , 2012, IEEE Signal Processing Letters.

[15]  Umit Ozguner,et al.  Controllability, reachability, stabilizability and state reduction in automata , 1992, Proceedings of the 1992 IEEE International Symposium on Intelligent Control.

[16]  Guo-Qiang Zhang,et al.  Automata, Boolean Matrices, and Ultimate Periodicity , 1999, Inf. Comput..

[17]  Thomas Gloe,et al.  Efficient estimation and large-scale evaluation of lateral chromatic aberration for digital image forensics , 2010, Electronic Imaging.

[18]  Andrew Zisserman,et al.  Metric rectification for perspective images of planes , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[19]  H. Farid,et al.  Image forgery detection , 2009, IEEE Signal Processing Magazine.

[20]  Hany Farid,et al.  Exposing Digital Forgeries in Complex Lighting Environments , 2007, IEEE Transactions on Information Forensics and Security.

[21]  Henri P. Gavin,et al.  The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems c © , 2013 .

[22]  Hany Farid,et al.  Exposing digital forgeries from 3-D lighting environments , 2010, 2010 IEEE International Workshop on Information Forensics and Security.

[23]  Yiguang Hong,et al.  Matrix approach to stabilizability of deterministic finite automata , 2013, 2013 American Control Conference.

[24]  G. Metze,et al.  Transition Matrices of Sequential Machines , 1959 .

[25]  James H. Elder,et al.  Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery , 2008, ECCV.

[26]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005 .

[27]  Matthias Kirchner Efficient estimation of CFA pattern configuration in digital camera images , 2010, Electronic Imaging.

[28]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[29]  Yiguang Hong,et al.  Matrix expression and reachability analysis of finite automata , 2012 .

[30]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[31]  Hong Yiguang,et al.  Bi-decomposition analysis and algorithm of automata based on semi-tensor product , 2012, Proceedings of the 31st Chinese Control Conference.