Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
This work addresses image manipulation detection for forensic applications, but it is incremental as it combines existing methods.
The paper tackled the problem of detecting image forgeries by combining copy-move and resampling detection methods, resulting in an 8%-10% increase in detection rates on datasets including the NIST Nimble Challenge.
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.