Copy-Move Forgery Classification via Unsupervised Domain Adaptation
This addresses the challenge of insufficient publicly available datasets for image forgery detection, particularly in forensic applications.
The paper tackles the problem of detecting copy-move forgeries in images when labeled data is scarce by creating a synthetic forged dataset using deep semantic image inpainting and applying an unsupervised domain adaptation network for classification.
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.