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.

翻译：复制-移动伪造检测旨在检测疑似伪造图像中的重复区域，而基于深度学习的复制-移动伪造检测方法正日益兴起。这类深度学习方法高度依赖合成训练数据，在面临新任务时性能会下降。本文提出一种名为CMFDFormer的Transformer式复制-移动伪造检测网络，并创新性地提出PCSD（池化立方体与条带蒸馏）持续学习框架，以帮助CMFDFormer处理新任务。CMFDFormer由MiT（混合Transformer）主干网络和PHD（可插拔混合解码器）掩码预测网络组成。MiT主干网络是一种Transformer式网络，基于对CNN式和MLP式主干网络的综合分析而采用。PHD网络基于自相关计算、层次化特征整合、多尺度循环全连接块和掩码重建块构建，可适配不同风格的特征提取器，实现层级化多尺度信息提取，并达到可比较的性能。最后，我们提出PCSD持续学习框架，以提升伪造检测能力并避免处理新任务时的灾难性遗忘。该持续学习框架通过约束PHD网络中的中间特征，同时利用立方体池化和条带池化的优势。在公开数据集上的大量实验表明，CMFDFormer性能优越，且PCSD持续学习框架效果显著。