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.

该论文提出了一种针对未知生成器的异常检测视角下的对抗式师生差异感知框架，通过采用对抗学习训练特征增强器，促使真实图像与教师模型之间的输出差异较小，而假图像的输出差异较大，从而解决了未知生成器检测问题。实验结果在公共基准测试中达到了最先进水平，可视化结果显示在面对各种类型的生成器时仍能保持较大的输出差异。