  With the rapid progress of generation technology, it has become necessary to
attribute the origin of fake images. Existing works on fake image attribution
perform multi-class classification on several Generative Adversarial Network
(GAN) models and obtain high accuracies. While encouraging, these works are
restricted to model-level attribution, only capable of handling images
generated by seen models with a specific seed, loss and dataset, which is
limited in real-world scenarios when fake images may be generated by privately
trained models. This motivates us to ask whether it is possible to attribute
fake images to the source models' architectures even if they are finetuned or
retrained under different configurations. In this work, we present the first
study on Deepfake Network Architecture Attribution to attribute fake images on
architecture-level. Based on an observation that GAN architecture is likely to
leave globally consistent fingerprints while traces left by model weights vary
in different regions, we provide a simple yet effective solution named DNA-Det
for this problem. Extensive experiments on multiple cross-test setups and a
large-scale dataset demonstrate the effectiveness of DNA-Det.
