Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

TL;DR

This paper presents a combined approach using copy-move and resampling detection techniques to improve the accuracy of image forgery detection, demonstrating significant performance gains on multiple datasets.

Contribution

It introduces a novel framework that integrates copy-move pre-filtering with deep learning resampling detection, enhancing overall detection accuracy.

Findings

Detection rates increased by 8%-10% with the combined approach

Effective on diverse datasets including NIST Nimble Challenge

Complementary use of copy-move and resampling detection improves robustness

Abstract

Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimental results on various datasets including the 2017 NIST Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and tampered images shows that there is a consistent increase of 8%-10% in detection rates, when copy-move algorithm is combined with different resampling detection algorithms.