Reliability assessment of principal point estimates for forensic applications

Abstract Although quite recent as a forensic research domain, computer vision analysis of scenes is likely to become more and more important in the near future, thanks to its robustness to image alterations at the signal level, such as image compression and filtering. However, the experimental assessment of vision-based forensic algorithms is a particularly critical task, since they cannot be tested on massive amounts of data, and their performance can heavily depend on user skill. In this paper we investigate on the accuracy and reliability of a vision-based, user-supervised method for the estimation of the camera principal point, to be used in cropping and splicing detection. Results of an extensive experimental evaluation show how the estimation accuracy depends on perspective conditions as well as on the selected image features. Such evidence led us to define a novel visual feature, referred to as Minimum Vanishing Angle, which can be used to assess the reliability of the method.

[1]  Nimit Dhulekar.  Exposing Digital Forgeries in Complex Lighting Environments , 2010 .

[2]  Jana Kosecka,et al.  Efficient computation of vanishing points , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[3]  Luc Van Gool,et al.  The cascaded Hough transform as an aid in aerial image interpretation , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[4]  Vijay H. Mankar,et al.  Digital image forgery detection using passive techniques: A survey , 2013, Digit. Investig..

[5]  Carsten Rother,et al.  A New Approach for Vanishing Point Detection in Architectural Environments , 2000, BMVC.

[6]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[7]  Shaozhang Niu,et al.  Exposing digital image forgeries by detecting inconsistencies in principal point , 2011, 2011 International Conference on Computer Science and Service System (CSSS).

[8]  M. Isard,et al.  Automatic Camera Calibration from a Single Manhattan Image , 2002, ECCV.

[9]  Alessandro Piva,et al.  Image Forgery Localization via Block-Grained Analysis of JPEG Artifacts , 2012, IEEE Transactions on Information Forensics and Security.

[10]  Hany Farid,et al.  Exposing photo manipulation from user-guided 3D lighting analysis , 2015, Electronic Imaging.

[11]  Eric Maisel,et al.  Using vanishing points for camera calibration and coarse 3D reconstruction from a single image , 2000, The Visual Computer.

[12]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[13]  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.

[14]  A. Piva.  An Overview on Image Forensics , 2013 .

[15]  James F. O'Brien,et al.  Exposing photo manipulation with inconsistent shadows , 2013, TOGS.

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

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

[18]  Mo Chen,et al.  Determining Image Origin and Integrity Using Sensor Noise , 2008, IEEE Transactions on Information Forensics and Security.

[19]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[20]  Christian Riess,et al.  Exposing Digital Image Forgeries by Illumination Color Classification , 2013, IEEE Transactions on Information Forensics and Security.

[21]  Marc Pollefeys,et al.  3-line RANSAC for orthogonal vanishing point detection , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Sing Bing Kang,et al.  Emerging Topics in Computer Vision , 2004 .

[23]  Horst Bischof,et al.  Online Auto-Calibration in Man-Made Worlds , 2005, Digital Image Computing: Techniques and Applications (DICTA'05).

[24]  Alessandro Piva,et al.  Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts , 2012, IEEE Transactions on Information Forensics and Security.

[25]  Andrea Fusiello,et al.  Hierarchical structure-and-motion recovery from uncalibrated images , 2015, Comput. Vis. Image Underst..

[26]  Hany Farid,et al.  Detecting Photographic Composites of People , 2008, IWDW.

[27]  ZhangZhengyou.  A Flexible New Technique for Camera Calibration , 2000 .

[28]  Andrea Fusiello,et al.  Robust Multiple Structures Estimation with J-Linkage , 2008, ECCV.

[29]  Bin Li,et al.  Revealing the Trace of High-Quality JPEG Compression Through Quantization Noise Analysis , 2015, IEEE Transactions on Information Forensics and Security.

[30]  Alberto Del Bimbo,et al.  Camera Calibration with Two Arbitrary Coaxial Circles , 2006, ECCV.

[31]  B. Caprile,et al.  Using vanishing points for camera calibration , 1990, International Journal of Computer Vision.

[32]  H. Farid.  A Survey of Image Forgery Detection , 2008 .

[33]  Alessandro Piva,et al.  Image splicing detection based on general perspective constraints , 2015, 2015 IEEE International Workshop on Information Forensics and Security (WIFS).

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

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

[36]  Shaziya .P.S. Khan,et al.  Exposing Digital Image Forgeries by Illumination Color Classification , 2015 .

[37]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Alan L. Yuille,et al.  Manhattan World: compass direction from a single image by Bayesian inference , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

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

[41]  Xinxin Niu,et al.  Exposing photo manipulation with inconsistent perspective geometry , 2014 .

[42]  Jean-Philippe Tardif,et al.  Non-iterative approach for fast and accurate vanishing point detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[43]  Min Wu,et al.  Information Forensics: An Overview of the First Decade , 2013, IEEE Access.

[44]  Yiannis Kompatsiaris,et al.  Detecting image splicing in the wild (WEB) , 2015, 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).