Image Splicing Detection Using Inherent Lens Radial Distortion

Image splicing is a common form of image forgery. Such alterations may leave no visual clues of tampering. In recent works camera characteristics consistency across the image has been used to establish the authenticity and integrity of digital images. Such constant camera characteristic properties are inherent from camera manufacturing processes and are unique. The majority of digital cameras are equipped with spherical lens and this introduces radial distortions on images. This aberration is often disturbed and fails to be consistent across the image, when an image is spliced. This paper describes the detection of splicing operation on images by estimating radial distortion from different portions of the image using line-based calibration. For the first time, the detection of image splicing through the verification of consistency of lens radial distortion has been explored in this paper. The conducted experiments demonstrate the efficacy of our proposed approach for the detection of image splicing on both synthetic and real images.

[1]  Hellward Broszio,et al.  Robust line-based calibration of lens distortion from a single view , 2003 .

[2]  Nenghai Yu,et al.  Passive detection of doctored JPEG image via block artifact grid extraction , 2009, Signal Process..

[3]  Alin C. Popescu,et al.  Exposing digital forgeries in color filter array interpolated images , 2005, IEEE Transactions on Signal Processing.

[4]  H Farid,et al.  Blind removal of lens distortion. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

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

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

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

[8]  Shih-Fu Chang,et al.  Physics-motivated features for distinguishing photographic images and computer graphics , 2005, ACM Multimedia.

[9]  Frederic Devernay A Non-Maxima Suppression Method for Edge Detection with Sub-Pixel Accuracy , 1995 .

[10]  Jiwu Huang,et al.  Robust Detection of Region-Duplication Forgery in Digital Image , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[11]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005, IEEE Transactions on Signal Processing.

[13]  Jiwu Huang,et al.  A survey of passive technology for digital image forensics , 2007, Frontiers of Computer Science in China.

[14]  Edmund Y Lam,et al.  Automatic source camera identification using the intrinsic lens radial distortion. , 2006, Optics express.

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

[16]  Wei Su,et al.  Image splicing detection using 2-D phase congruency and statistical moments of characteristic function , 2007, Electronic Imaging.

[17]  Olivier D. Faugeras,et al.  Automatic calibration and removal of distortion from scenes of structured environments , 1995, Optics & Photonics.

[18]  Shih-Fu Chang,et al.  A model for image splicing , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

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

[20]  Shih-Fu Chang,et al.  Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[21]  Hany Farid,et al.  Exposing digital forgeries through chromatic aberration , 2006, MM&Sec '06.

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

[23]  Shih-Fu Chang,et al.  Blind detection of photomontage using higher order statistics , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).