  Recent advancements in Generative Adversarial Networks (GANs) enable the
generation of highly realistic images, raising concerns about their misuse for
malicious purposes. Detecting these GAN-generated images (GAN-images) becomes
increasingly challenging due to the significant reduction of underlying
artifacts and specific patterns. The absence of such traces can hinder
detection algorithms from identifying GAN-images and transferring knowledge to
identify other types of GAN-images as well. In this work, we present the
Transferable GAN-images Detection framework T-GD, a robust transferable
framework for an effective detection of GAN-images. T-GD is composed of a
teacher and a student model that can iteratively teach and evaluate each other
to improve the detection performance. First, we train the teacher model on the
source dataset and use it as a starting point for learning the target dataset.
To train the student model, we inject noise by mixing up the source and target
datasets, while constraining the weight variation to preserve the starting
point. Our approach is a self-training method, but distinguishes itself from
prior approaches by focusing on improving the transferability of GAN-image
detection. T-GD achieves high performance on the source dataset by overcoming
catastrophic forgetting and effectively detecting state-of-the-art GAN-images
with only a small volume of data without any metadata information.
