  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 handle
new tasks. CMFDFormer consists of a MiT (Mix Transformer) backbone network and
a PHD (Pluggable Hybrid Decoder) mask prediction network. The MiT backbone
network is a Transformer-style network which is adopted on the basis of
comprehensive analyses with CNN-style and MLP-style backbones. The PHD network
is constructed based on self-correlation computation, hierarchical feature
integration, a multi-scale cycle fully-connected block and a mask
reconstruction block. The PHD network is applicable to feature extractors of
different styles for hierarchical multi-scale information extraction, achieving
comparable performance. Last but not least, we propose a PCSD continual
learning framework to improve the forgery detectability and avoid catastrophic
forgetting when handling new tasks. Our continual learning framework restricts
intermediate features from the PHD network, and takes advantage of both cube
pooling and strip pooling. Extensive experiments on publicly available datasets
demonstrate the good performance of CMFDFormer and the effectiveness of the
PCSD continual learning framework.
