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GenDet: Towards Good Generalizations for AI-Generated Image Detection

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arxiv 2312.08880 v1 pith:A5TNS2SD submitted 2023-12-12 cs.CV

GenDet: Towards Good Generalizations for AI-Generated Image Detection

Mingjian Zhu , Hanting Chen , Mouxiao Huang , Wei Li , Hailin Hu , Jie Hu , Yunhe Wang This is my paper
classification cs.CV
keywords fakeimagesgeneratorsoutputdetectiondiscrepanciesrealwhen
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 10 Pith papers

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