  With the progress in AI-based facial forgery (i.e., deepfake), people are
increasingly concerned about its abuse. Albeit effort has been made for
training classification (also known as deepfake detection) models to recognize
such forgeries, existing models suffer from poor generalization to unseen
forgery technologies and high sensitivity to changes in image/video quality. In
this paper, we advocate adversarial training for improving the generalization
ability to both unseen facial forgeries and unseen image/video qualities. We
believe training with samples that are adversarially crafted to attack the
classification models improves the generalization ability considerably.
Considering that AI-based face manipulation often leads to high-frequency
artifacts that can be easily spotted by models yet difficult to generalize, we
further propose a new adversarial training method that attempts to blur out
these specific artifacts, by introducing pixel-wise Gaussian blurring models.
With adversarial training, the classification models are forced to learn more
discriminative and generalizable features, and the effectiveness of our method
can be verified by plenty of empirical evidence. Our code will be made publicly
available.
