DocumentCode :
3411184
Title :
Forensic techniques for classifying scanner, computer generated and digital camera images
Author :
Khanna, Nitin ; Chiu, George T -C ; Allebach, Jan P. ; Delp, Edward J.
Author_Institution :
Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
1653
Lastpage :
1656
Abstract :
Digital images can be captured or generated by a variety of sources including digital cameras, scanners and computer graphics softwares. In many cases it is important to be able to determine the source of a digital image such as for criminal and forensic investigation. This paper presents methods for distinguishing between an image captured using a digital camera, a computer generated image and an image captured using a scanner. The method proposed here is based on the differences in the image generation processes used in these devices and is independent of the image content. The method is based on using features of the residual pattern noise that exist in images obtained from digital cameras and scanners. The residual noise present in computer generated images does not have structures similar to the pattern noise of cameras and scanners. The experiments show that a feature based approach using an SVM classifier gives high accuracy.
Keywords :
image classification; police; support vector machines; SVM classifier; classifying scanner; computer generated image; computer graphics; digital camera image; image forensics; image generation processe; pattern noise; residual pattern noise; scanner image; Digital cameras; Digital images; Forensics; Frequency estimation; Image generation; Noise reduction; Optical noise; Optical sensors; Semiconductor device noise; Support vector machines; computer graphics; digital camera; image forensics; pattern noise; scanners;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4517944
Filename :
4517944
Link To Document :
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