  The misuse of AI imagery can have harmful societal effects, prompting the
creation of detectors to combat issues like the spread of fake news. Existing
methods can effectively detect images generated by seen generators, but it is
challenging to detect those generated by unseen generators. They do not
concentrate on amplifying the output discrepancy when detectors process real
versus fake images. This results in a close output distribution of real and
fake samples, increasing classification difficulty in detecting unseen
generators. This paper addresses the unseen-generator detection problem by
considering this task from the perspective of anomaly detection and proposes an
adversarial teacher-student discrepancy-aware framework. Our method encourages
smaller output discrepancies between the student and the teacher models for
real images while aiming for larger discrepancies for fake images. We employ
adversarial learning to train a feature augmenter, which promotes smaller
discrepancies between teacher and student networks when the inputs are fake
images. Our method has achieved state-of-the-art on public benchmarks, and the
visualization results show that a large output discrepancy is maintained when
faced with various types of generators.
