Forensic classification of imaging sensor types

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

doi:10.1117/12.705849

1 March 2007 Forensic classification of imaging sensor types

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

Proceedings Volume 6505, Security, Steganography, and Watermarking of Multimedia Contents IX; 65050U (2007) https://doi.org/10.1117/12.705849
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

Digital images can be captured or generated by a variety of sources including digital cameras and scanners. In many cases it is important to be able to determine the source of a digital image. Methods exist to authenticate images generated by digital cameras or scanners, however they rely on prior knowledge of the image source (camera or scanner). This paper presents methods for determining the class of the image source (camera or scanner). The method is based on using the differences in pattern noise correlations that exist between digital cameras and scanners. To improve the classification accuracy a feature vector based approach using an SVM classifier is used to classify the pattern noise.

Citation Download Citation

Nitin Khanna, Aravind K. Mikkilineni, George T. C. Chiu, Jan P. Allebach, and Edward J. Delp "Forensic classification of imaging sensor types", Proc. SPIE 6505, Security, Steganography, and Watermarking of Multimedia Contents IX, 65050U (1 March 2007); https://doi.org/10.1117/12.705849

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