Intensity and Rescale Invariant Copy Move Forgery Detection Techniques

TL;DR

This paper introduces novel copy-move forgery detection algorithms that are invariant to rescaling and intensity changes, enhancing the reliability of digital image authentication.

Contribution

It proposes two new algorithms using Hus invariant moments and DCT to detect forgeries despite rescaling and intensity modifications.

Findings

Algorithms effectively detect forgeries with rescaling.

Algorithms effectively detect forgeries with intensity changes.

Demonstrated robustness through quantitative and qualitative experiments.

Abstract

In this contemporary world digital media such as videos and images behave as an active medium to carry valuable information across the globe on all fronts. However there are several techniques evolved to tamper the image which has made their authenticity untrustworthy. CopyMove Forgery CMF is one of the most common forgeries present in an image where a cluster of pixels are duplicated in the same image with potential postprocessing techniques. Various state-of-art techniques are developed in the recent years which are effective in detecting passive image forgery. However most methods do fail when the copied image is rescaled or added with certain intensity before being pasted due to de-synchronization of pixels in the searching process. To tackle this problem the paper proposes distinct novel algorithms which recognize a unique approach of using Hus invariant moments and Discreet Cosine Transformations DCT to attain the desired rescale invariant and intensity invariant CMF detection techniques respectively. The experiments conducted quantitatively and qualitatively demonstrate the effectiveness of the algorithm.