FD-GAN: Generalizable and Robust Forgery Detection via Generative Adversarial Networks.

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. [ABSTRACT FROM AUTHOR]