A classifier design for detecting image manipulations

In this paper we present a framework for digital image forensics. Based on the assumptions that some processing operations must be done on the image before it is doctored and an expected measurable distortion after processing an image, we design classifiers that discriminates between original and pr...

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Bibliographic Details
Main Authors: Avcibas, I., Bayram, S., Memon, N., Ramkumar, M., Sankur, B.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Brightness
Computer science
Computer science
control theory
systems
Design engineering
Digital images
Distortion measurement
Exact sciences and technology
Information science
Law
Legal factors
Pattern recognition. Digital image processing. Computational geometry
Testing
Watermarking
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Description
Summary:In this paper we present a framework for digital image forensics. Based on the assumptions that some processing operations must be done on the image before it is doctored and an expected measurable distortion after processing an image, we design classifiers that discriminates between original and processed images. We propose a novel way of measuring the distortion between two images, one being the original and the other processed. The measurements are used as features in classifier design. Using these classifiers we test whether a suspicious part of a given image has been processed with a particular method or not. Experimental results show that with a high accuracy we are able to tell if some part of an image has undergone a particular or a combination of processing methods.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2004.1421647