CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning

arXiv:2311.13263v2 Announce Type: replace
Abstract: Copy-move forgery detection aims at detecting duplicated regions in a suspected forged image, and deep learning based copy-move forgery detection methods are in the ascendant. These deep learning based methods heavily rely on synthetic training data, and the performance will degrade when facing new tasks. In this paper, we propose a Transformer-style copy-move forgery detection network named as CMFDFormer, and provide a novel PCSD (Pooled Cube and Strip Distillation) continual learning framework to help CMFDFormer …

abstract arxiv continual copy cs.cv data deep learning detection detection methods forgery image performance synthetic tasks training training data transformer type will