Copy-Move Forgery Classification via Unsupervised Domain Adaptation

TL;DR

This paper introduces an unsupervised domain adaptation method for copy-move forgery detection, utilizing a synthetic dataset generated by deep semantic inpainting to improve detection in data-scarce scenarios.

Contribution

It presents a novel approach combining synthetic data creation with unsupervised domain adaptation for improved forgery classification.

Findings

Effective detection of copy-move forgeries in unlabeled data

Synthetic dataset enhances model training and robustness

Unsupervised method performs well without labeled data

Abstract

In the current era, image manipulation is becoming increasingly easier, yielding more natural looking images, owing to the modern tools in image processing and computer vision techniques. The task of the segregation of forged images has become very challenging. To tackle such problems, publicly available datasets are insufficient. In this paper, we propose to create a synthetic forged dataset using deep semantic image inpainting algorithm. Furthermore, we use an unsupervised domain adaptation network to detect copy-move forgery in images. Our approach can be helpful in those cases, where the classification of data is unavailable.