Exposing Digital Image Forgeries by Illumination Color Classification using Sift Algorithm

A digital image is fundamentally composed of a series of pixels, a word derived from combining picture and element. Traditionally, a photograph implies the truth of what has happened. Our life is full of digital images and tampering these digital images can be done easily with the help of powerful image editing software’s mainly to cover up the truthfulness of the photographs which often serve as evidence in court, in forensic investigation, criminal investigation, surveillance systems, intelligence services, medical imaging, and journalism. It is very easily to change the information represented by an image without leaving any obvious traces of tampering by using photo editing software like photo shop, photo grids which are easily available in the internet. In this work we presents a method to detect image retouching, image splicing and copy move attacks. Image splicing means specifically copying and pasting a person or any things from an image to another. Our approach is machine-learning based and requires minimal user interaction. The technique is applicable to images containing two or more people and requires no expert interaction for the tampering decision. To achieve this, we incorporate information from ‘physics’ and ‘statistical-based’ illuminant estimators on image regions of similar material.