Call For Paper Volume:4 Issue:10 Oct'2017 |

Digital Image Forgery Detection Using Improved Illumination Detection

Publication Date : 31/01/2015



Author(s) :

Ms. V. Shanmugapriya , Dr. S. Sathappan , Mr. R. Subramanian.


Volume/Issue :
Volume 2
,
Issue 1
(01 - 2015)



Abstract :

Image processing methods are widely used in advertisement, magazines, blogs, website, television and more. When the digital images took their role, Happening of crimes and escaping from the crimes happened becomes easier. To be with lawful, No one should be punished for not commencing a crime, to help them this application can be used. The identification using color edge method will give a exact detection of the crime and the forgeries that has been done in the digital image.    Image composition or splicing methods are used to discover the image forgeries. The approach is machine-learning- based and requires minimal user interaction and this technique is applicable to images containing two or more people and requires no expert interaction for the tampering decision. The obtained result by the classification performance using an SVM (Super Vector Machine) meta-fusion classifier and It yields detection rates of 86% on a new benchmark dataset consisting of 200 images, and 83% on 50 images that were collected from the Internet.    The further improvements can be achieved when more advanced illuminant color estimators become available. Bianco and Schettini has proposed a machine-learning based illuminant estimator particularly for faces which would help us in this for more accurate prediction. Effective skin detection methods have been developed in the computer vision literature and this method also helps us, in detecting pornography compositions which, according to forensic practitioners, have become increasingly common nowadays. 


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Digital Image Forgery Detection Using Improved Illumination Detection

January 29, 2015