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

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.

Extraction of image resampling using correlation aware convolution neural networks for image tampering detection

“…The research hypothesis is that the state-of-art tampering detection methodologies [36] using deep learning techniques are effective in detecting various types of tampering attacks. However, existing models predominantly achieves poor results when hybrid attacks are introduced into an image; for example, when a copy-clone attack is transformed by rotation, scaling, and compression.…”