Detection of Forgery Part in Forgery Image Using Color Intensity

Since many decade’s photographs have been used to document space time events and they have often served
as evidence in courts. This process is very time consuming and requires expect knowledge, although
photographers are able to create composite of analog pictures. Powerful digital image editing software
makes images modifications straightforward. Questions pictures as evidence for real world events, this
undermines our trust in photographs and in particular. Image composition or splicing is a one of the most
common forms of photographic manipulation. In the color of the illumination of images, we propose a
forgery detection method that exploits subtle inconsistencies. Our approach is machine learning based and
requires minimal user interaction. No expert interaction for the tampering decision, the technique is
applicable to images containing two or more people. On image regions of similar material, to achieve this
we incorporate information from physics and statistical based illuminant estimators. We extract texture and
edge based features which are then provided to a machine learning approach for automatic decision
making. The classification performance using an SVM meta-fusion classifier is promising. It yields detection
rates of 86% on a new benchmark dataset consisting of 200 images, and83% on 50 images that were
collected from the Internet.