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.

提出了一种名为CMFDFormer的变压器风格的复制移动伪造检测网络，并提供了一种新颖的PCSD（Pooled Cube and Strip Distillation）连续学习框架来帮助CMFDFormer处理新任务。通过在综合分析CNN风格和MLP风格骨干网络的基础上采用变压器风格网络作为骨干网络，并使用基于自相关计算、分层特征融合、多尺度循环全连接块和掩码重建块构建PHD网络，实现不同风格的特征提取器的分层多尺度信息提取，从而达到可比较的性能。此外，提出了一种PCSD连续学习框架，以改善伪造的可检测性并在处理新任务时避免灾难性遗忘，该框架限制了PHD网络的中间特征，并利用了立方体池和条带池两者的优势。针对公开可用数据集进行了大量实验，证明了CMFDFormer的良好性能和PCSD连续学习框架的有效性。

CMFDFormer: 基于Transformer的连续学习的复制-移动伪造检测