On parameterization of block based copy-move forgery detection techniques

With high increase in cyber-crime along with continuous development in multimedia processing and editing technologies, the credibility of digital images is highly at stake in the present day. Digital images act as the major source of legal evidence in various domains such as media, broadcast and legal industries. Hence any form of illegal modifications to them is intolerable. Recently a lot of researchers have focused on detection and control of digital image forgeries. However the literature lacks a standard way of evaluating and comparing the efficiencies of diverse forgery detection techniques. In this paper we propose a standard platform for estimating the efficiencies of state-of-the-art digital image forgery detection techniques. In this work we have dealt with a specific class of digital image forgery: the Copy-Move forgery, which is one of most prevalent forms of attack on digital images. We have compared and analyzed different copy-move forgery detection techniques using the proposed parameters. Our results prove the efficiency of the proposed parameterization and help to select the most suitable scheme according to the user's requirements.

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

[2]  Frank Y. Shih,et al.  Digital Watermarking and Steganography: Fundamentals and Techniques , 2007 .

[3]  Jessica Fridrich,et al.  Detection of Copy-Move Forgery in Digital Images , 2004 .

[4]  Shih-Fu Chang,et al.  Classifying Photographic and Photorealistic Computer Graphic Images using Natural Image Statistics , 2006 .

[5]  Jie Wu,et al.  Detecting Differences between Photographs and Computer Generated Images , 2006, SPPRA.

[6]  Xiaochun Cao,et al.  Detecting photographic composites using two-view geometrical constraints , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[7]  Jan Lukás,et al.  Detecting digital image forgeries using sensor pattern noise , 2006, Electronic Imaging.

[8]  Hany Farid,et al.  Exposing Digital Forgeries From JPEG Ghosts , 2009, IEEE Transactions on Information Forensics and Security.

[9]  Alexei A. Efros,et al.  Using Color Compatibility for Assessing Image Realism , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[10]  Feng Pan,et al.  Discriminating between photorealistic computer graphics and natural images using fractal geometry , 2009, Science in China Series F: Information Sciences.

[11]  Xiaochun Cao,et al.  Detecting photographic composites using shadows , 2009, 2009 IEEE International Conference on Multimedia and Expo.

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

[13]  Tiegang Gao,et al.  A robust detection algorithm for copy-move forgery in digital images. , 2012, Forensic science international.

[14]  Xunyu Pan,et al.  Detecting image region duplication using SIFT features , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Christian Riess,et al.  Automated Image Forgery Detection through Classification of JPEG Ghosts , 2012, DAGM/OAGM Symposium.

[16]  Nasir Memon,et al.  Digital Image Forensics: There is More to a Picture than Meets the Eye , 2012 .

[17]  Ruchira Naskar,et al.  Digital Forensic Technique for Double Compression Based JPEG Image Forgery Detection , 2014, ICISS.

[18]  Norman Wang,et al.  How Real is Really? A Perceptually Motivated System for Quantifying Visual Realism in Digital Images , 2011, 2011 International Conference on Multimedia and Signal Processing.

[19]  Hany Farid,et al.  Exposing digital forgeries by detecting inconsistencies in lighting , 2005, MM&Sec '05.

[20]  Luo Wei,et al.  Robust Detection of Region-Duplication Forgery in Digital Image , 2007 .