Scanner identification with extension to forgery detection

Nitin Khanna; George T. C. Chiu; Jan P. Allebach; Edward J. Delp

doi:10.1117/12.772048

18 March 2008 Scanner identification with extension to forgery detection

Nitin Khanna, George T. C. Chiu, Jan P. Allebach, Edward J. Delp

Proceedings Volume 6819, Security, Forensics, Steganography, and Watermarking of Multimedia Contents X; 68190G (2008) https://doi.org/10.1117/12.772048
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

Digital images can be obtained through a variety of sources including digital cameras and scanners. With rapidly increasing functionality and ease of use of image editing software, determining authenticity and identifying forged regions, if any, is becoming crucial for many applications. This paper presents methods for authenticating and identifying forged regions in images that have been acquired using flatbed scanners. The methods are based on using statistical features of imaging sensor pattern noise as a fingerprint for the scanner. An anisotropic local polynomial estimator is used for obtaining the noise patterns. A SVM classifier is trained for using statistical features of pattern noise for classifying smaller blocks of an image. This feature vector based approach is shown to identify the forged regions with high accuracy.

Citation Download Citation

Nitin Khanna, George T. C. Chiu, Jan P. Allebach, and Edward J. Delp "Scanner identification with extension to forgery detection", Proc. SPIE 6819, Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, 68190G (18 March 2008); https://doi.org/10.1117/12.772048

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available