Generalization across various forgeries and robustness against corruption are pressing challenges of forgery detection. Although previous works boost generalization with the help of data augmentations, they rarely consider the robustness against corruption. To tackle these two issues of generalization and robustness simultaneously, in this paper, we propose a novel forgery detection generative adversarial network (FD-GAN), which consists of two generators (a blend-based generator and a transfer-based generator) and a discriminator. Concretely, the blend-based generator and the transfer-based generator can adaptively create challenging synthetic images with more flexible strategies to improve generalization. Besides, the discriminator is designed to judge whether the input is synthetic and predicts the manipulated regions with a collaboration of spatial and frequency branches. And the frequency branch utilizes Low-rank Estimation algorithms to filter out adversarial corruption in the input for robustness. Furthermore, to present a deeper understanding of FD-GAN, we apply theoretical analysis on forgery detection, which provides some guidelines on data augmentations for improving generalization and mathematical support for robustness. Extensive experiments demonstrate that FD-GAN exhibits better generalization and robustness. For example, FD-GAN outperforms 14 existing methods on 3 benchmarks in generalization evaluation, and it separately improves the performance against 6 kinds of adversarial attacks and 7 types of distortions by 16.2% and 2.3% on average in robustness evaluation.

各种伪造品的泛化和反腐败的稳健性是伪造品检测面临的紧迫挑战。尽管以前的工作借助数据增强来提高泛化能力，但他们很少考虑针对腐败的鲁棒性。为了同时解决泛化和鲁棒性这两个问题，在本文中，我们提出了一种新颖的伪造检测生成对抗网络（FD-GAN），它由两个生成器（基于混合的生成器和基于传输的生成器）和一个鉴别器。具体来说，基于混合的生成器和基于传输的生成器可以通过更灵活的策略自适应地创建具有挑战性的合成图像，以提高泛化能力。此外，鉴别器旨在判断输入是否是合成的，并通过空间和频率分支的协作来预测被操纵的区域。频率分支利用低秩估计算法来过滤掉输入中的对抗性损坏，以实现鲁棒性。此外，为了更深入地理解 FD-GAN，我们应用了伪造检测的理论分析，这为数据增强提供了一些指导，以提高泛化性和鲁棒性的数学支持。大量实验表明 FD-GAN 表现出更好的泛化性和鲁棒性。例如，FD-GAN在泛化评估中在3个基准上优于14种现有方法，在鲁棒性评估中针对6种对抗攻击和7种扭曲的性能分别平均提高了16.2%和2.3%。