Exposing image splicing with inconsistent local noise variances

Image splicing is a simple and common image tampering operation, where a selected region from an image is pasted into another image with the aim to change its content. In this paper, based on the fact that images from different origins tend to have different amount of noise introduced by the sensors or post-processing steps, we describe an effective method to expose image splicing by detecting inconsistencies in local noise variances. Our method estimates local noise variances based on an observation that kurtosis values of natural images in band-pass filtered domains tend to concentrate around a constant value, and is accelerated by the use of integral image. We demonstrate the efficacy and robustness of our method based on several sets of forged images generated with image splicing.

[1]  L. Dworsky.  An Introduction to Probability , 2008 .

[2]  Nasir D. Memon,et al.  Image manipulation detection , 2006, J. Electronic Imaging.

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

[4]  Hany Farid,et al.  Photo Fakery and Forensics , 2009, Adv. Comput..

[5]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[6]  Søren I. Olsen,et al.  Estimation of Noise in Images: An Evaluation , 1993, CVGIP Graph. Model. Image Process..

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

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

[9]  Miroslav Goljan,et al.  Using sensor pattern noise for camera model identification , 2008, 2008 15th IEEE International Conference on Image Processing.

[10]  Eero P. Simoncelli,et al.  Nonlinear Extraction of Independent Components of Natural Images Using Radial Gaussianization , 2009, Neural Computation.

[11]  Rongrong Wang,et al.  Detecting doctored images using camera response normality and consistency , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Xing Zhang,et al.  Exposing image forgery with blind noise estimation , 2011, MM&Sec '11.

[13]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[14]  R. Matzner,et al.  An SNR estimation algorithm using fourth-order moments , 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

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

[16]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[17]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[18]  W. B. Collis,et al.  Training Methods for Image Noise Level Estimation on Wavelet Components , 2004, EURASIP J. Adv. Signal Process..

[19]  Norman C. Beaulieu,et al.  A comparison of SNR estimation techniques for the AWGN channel , 2000, IEEE Trans. Commun..

[20]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[21]  Hany Farid,et al.  Statistical Tools for Digital Forensics , 2004, Information Hiding.

[22]  Babak Mahdian,et al.  Using noise inconsistencies for blind image forensics , 2009, Image Vis. Comput..

[23]  Chi-Keung Tang,et al.  Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis , 2009, Pattern Recognit..

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

[25]  Richard Szeliski,et al.  Automatic Estimation and Removal of Noise from a Single Image , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Miroslav Goljan,et al.  Digital camera identification from sensor pattern noise , 2006, IEEE Transactions on Information Forensics and Security.

[27]  Gerald Schaefer,et al.  UCID: an uncompressed color image database , 2003, IS&T/SPIE Electronic Imaging.

[28]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[29]  Charles M. Grinstead,et al.  Introduction to probability , 1999, Statistics for the Behavioural Sciences.

[30]  John Immerkær,et al.  Fast Noise Variance Estimation , 1996, Comput. Vis. Image Underst..

[31]  M. Bethge.  Factorial coding of natural images: how effective are linear models in removing higher-order dependencies? , 2006, Journal of the Optical Society of America. A, Optics, image science, and vision.

[32]  Yair Weiss,et al.  Scale invariance and noise in natural images , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  R. Unbehauen,et al.  Estimation of image noise variance , 1999 .

[34]  T. T. Soong,et al.  The joint estimation of signal and noise from the sum envelope , 1967, IEEE Trans. Inf. Theory.

[35]  J. H. Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .

[36]  Junfeng He,et al.  Detecting Doctored JPEG Images Via DCT Coefficient Analysis , 2006, ECCV.