  Rapid pace of generative models has brought about new threats to visual
forensics such as malicious personation and digital copyright infringement,
which promotes works on fake image attribution. Existing works on fake image
attribution mainly rely on a direct classification framework. Without
additional supervision, the extracted features could include many
content-relevant components and generalize poorly. Meanwhile, how to obtain an
interpretable GAN fingerprint to explain the decision remains an open question.
Adopting a multi-task framework, we propose a GAN Fingerprint Disentangling
Network (GFD-Net) to simultaneously disentangle the fingerprint from
GAN-generated images and produce a content-irrelevant representation for fake
image attribution. A series of constraints are provided to guarantee the
stability and discriminability of the fingerprint, which in turn helps
content-irrelevant feature extraction. Further, we perform comprehensive
analysis on GAN fingerprint, providing some clues about the properties of GAN
fingerprint and which factors dominate the fingerprint in GAN architecture.
Experiments show that our GFD-Net achieves superior fake image attribution
performance in both closed-world and open-world testing. We also apply our
method in binary fake image detection and exhibit a significant generalization
ability on unseen generators.
