Detection of Forgery Region by using Feature Extraction with SURF and Its Comparative Analysis with SIFT technique

Abstract

The invention of the internet has introduced the unimaginable growth and developments in the renowned research fields such as medicine, satellite imagery, image processing, security, biometrics, and genetics. The algorithms implemented in the 21^st century has made the human life more comfortable and secure, but the security to the original documents belongs to the authenticated person is remained as concerned in the digital image processing domain. A new study is proposed in this research paper to detect the forgery detection in accurate manner using the adaptive over-segmentation and feature point matching. The integration of the block-based and key point-based forgery detection methods is the key idea in the proposed study and the detection of the suspected regions are detected by the adaptive non-overlapping and irregular blocks and this process is carried out using the adaptive over-segmentation algorithm. The extraction of the feature points is performed by performing the matching between the each block and its features. The feature points are gradually replaced by using the super pixels in the proposed Forgery Region Extraction algorithm and then merge the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions; finally, it applies the morphological operation to the merged regions to generate the detected forgery regions. The proposed forgery detection algorithm achieves much better detection results even under various challenging conditions the earlier methods in all aspects. We will analyze the results obtained by the both SIFT and SURF and it is proved that the proposed technique

SURF is giving more satisfactory results by both subjective and objective analysis.