Passive Approaches for Image Forgery Detection

Pravin KakarPrimary Adviser: Asst. Prof. Sudha Natarajan, School of

Computer EngineeringCo-Adviser: Assoc. Prof. Ser Wee, School of Electrical &

Electronic Engineering

Outline

• Background & Motivation

• Research Objectives

• Existing Work

• Proposed Method

• Conclusion

Background & Motivation

• Tampered images have become pervasive– Counterfeiting– Evidence tampering– Antique Faking– Political Propaganda– Yellow Journalism– Scientific Research– Entertainment– Urban myths

• Forgeries made easy due to– Spread of digital cameras– Easy availability of image manipulation software

Research Objectives

• Devise new techniques

• Extend existing techniques

• Design efficient algorithms for forgery detection techniques

• Create benchmarking database

Forgery Detection Techniques

• Active

– Watermarking

– Signatures

• Passive

– Image Statistics

– Image Content

Passive Forgery Detection Techniques

• Farid, H.: A survey of image forgery detection. IEEE Signal Processing Magazine Vol. 2 (2009) 16-25

• Pixel-based Techniques:– Cloning Detection

• Farid et al, Dartmouth College Tech. Rep. TR2004-515 2004• Fridrich et al, Proc. DFRW 2003

– Resampling Detection• Popescu & Farid, IEEE Trans. Signal Processing, 2005

– Splicing Detection• Ng and Chang, Proc. IEEE ICIP, 2004

– Statistical Analyses• Bayram et al, Proc. ESPC, 2005

Passive Forgery Detection Techniques

• Format-based Techniques

– JPEG Quantization Table Analysis

• Farid, Dartmouth College Tech. Rep. TR2006-583, 2006

– Double Compression Detection

• Lukas et al, Proc. DFRW, 2003

• Popescu et al, Proc. 6th IWIH, 2004

– JPEG Blocking Detection

• Ye et al, IEEE ICME, 2007

Passive Forgery Detection Techniques

• Camera-based Techniques– Chromatic Aberration

• Johnson & Farid, ACM Conf. MM&Sec, 2006

– Color Filter Array Analysis• Popescu & Farid, IEEE Trans. Signal Processing, 2005

– Camera Response• Lin et al, Proc. CVPR, 2005

– Sensor Noise Analysis• Lukas et al, IEEE Trans. Inform. Forensics Sec., 2006

• Gou et al, Proc. IEEE ICIP, 2007

Passive Forgery Detection Techniques

• Physics-based Techniques (Johnson & Farid)

– Lighting

• ACM MM&Sec, 2005

• Proc. 9th IWIH, 2007

• IEEE Trans. Inform. Forensics Sec., 2007

– Geometry

• Dartmouth College Tech. Rep. TR2006-579, 2006

• Proc. 6th IWDW, 2007

Los Angeles Times, April 2003

Motion Blur

• Occurs due to

– Camera Shake

– Imaging fast-moving objects

Detecting Forgeries based on Blur Inconsistencies

• Hsiao & Pei, “Detecting Digital Tampering by Blur Estimation”, Proceedings of the First International Workshop on Systematic Approaches to Digital Forensic Engineering, 2005.

• Wang et al, “Digital Image Forgery Detection based on the consistency of defocus blur”, International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2008.

• Sutcu et al, “Tamper Detection based on the regularity of Wavelet Transform Coefficients”, Proceedings of the IEEE International Conference on Image Processing, 2007.

• Zhang & Su, “Detecting Logo-Removal Forgery by Inconsistencies of Blur”, Proceedings of the International Conference on Industrial Mechatronics & Automation”, 2009.

Estimating the Motion Blur

• Yitzhaky et al, “Direct Method for Restoration of Motion-blurred Images”, Journal of the Optical Society of America, June 1998.

• Zhang et al, “Estimation of motion parameters from blurred images”, Pattern Recognition Letters (21), 2000.

• Fergus et al, “Removing Camera Shake from a Single Photograph”, ACM SIGGRAPH, 2006.

• Rekleitis, “Optical Flow Recognition from the Power Spectrum of a Single Blurred Image”, Proceedings of the IEEE International Conference on Image Processing, 1996

Spectral Matting

• Technique for “soft” segmentation

A. Levin, A. Rav-Acha, and D. Lischinski. Spectral matting. In CVPR, 2007.

Blur Estimation from Matting Components

• Gradient of matting components may be used to estimate blur parameters

S. Dai and Y. Wu, “Motion from blur,” in CVPR 2008

Proposed Method

Divide image into overlapping blocks

Estimate motion blur for each block

Smooth motion blur estimates

Interpolate motion blur estimates to size of image

Segment image according to motion blur estimates

Accepted for oral presentation at ICME 2010: “Detecting Digital Image Forgeries through Inconsistent Motion Blur”

Blur Estimate Measures

Detecting Consistent and Inconsistent Regions

Comparison Results

Comparison Results

Future Work

• Incorporate improved blur estimation and segmentation techniques

• Extend motion blur-based techniques to video frames.

• Design efficient hardware for execution of the above algorithms

• Research new techniques for detecting digital forgeries

Thank you