This paper proposes an all-encompassing methodology called Strong Robust Copy-Move Forgery Detection Network based on Layer-by-Layer Decoupling Refinement (DRNet) which concentrates on detecting a pair of structurally complete similar areas (the source and the tampered area) in the copy-move forgery image by fully extracting the semantically irrelevant shallow information. The DRNet consists of two interacting modules: the Coarse Similarity Area Detection (CD) module and the Shallow Suppression Similarity Area Detection (SD) module. Specifically, the CD module is leveraged to obtain a coarse locating of similar target areas which also work as prior knowledge to guide the detection of the SD module. The SD module fully mines the suppressed information at the shallow layer of the network through layer-by-layer decoupling and uses it as a supplement to refine the coarse detection from the CD module. In addition, we propose a High-Order Self-Correlation Scheme (HS) by dealing with the problem of introducing noise during the process of utilizing the shallow feature to avoid false alarms and improve the robustness. The designed experiments are conducted on USC-ISI CMFD, CASIA CMFD, and CoMoFoD public datasets and the pixel-level F1 score tested by DRnet is improved by 2.27%, 3.82%, and 4.60% respectively than State-of-the-Art in CMFD.

中文翻译：

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基于逐层解耦细化的强健复制移动伪造检测网络

本文提出了一种基于逐层解耦细化（DRNet）的强鲁棒复制移动伪造检测网络（DRNet）的综合方法，该方法集中于检测图像中一对结构完整的相似区域（源区域和被篡改区域）。通过充分提取语义上不相关的浅层信息来复制移动伪造图像。DRNet 由两个相互作用的模块组成：粗略相似区域检测（CD）模块和浅抑制相似区域检测（SD）模块。具体来说，利用CD模块来获得相似目标区域的粗定位，这也可以作为先验知识来指导SD模块的检测。SD模块通过逐层解耦，充分挖掘网络浅层被抑制的信息，并以此作为补充，细化CD模块的粗略检测。此外，我们通过处理利用浅层特征过程中引入噪声的问题，提出了一种高阶自相关方案（HS），以避免误报并提高鲁棒性。设计的实验在USC-ISI CMFD、CASIA CMFD和CoMoFoD公共数据集上进行，DRnet测试的像素级F1分数比State-of-the-Art分别提高了2.27%、3.82%和4.60% CMFD。