Combating Pattern and Content Bias: Adversarial Feature Learning for Generalized AI-Generated Image Detection
In recent years, the rapid development of generative artificial intelligence technology has significantly lowered the barrier to creating high-quality fake images, posing a serious challenge to information authenticity and credibility. Existing generated image detection methods typically enhance generalization through model architecture or network design. However, their generalization performance remains susceptible to data bias, as the training data may drive models to fit specific generative patterns and content rather than the common features shared by images from different generative models (asymmetric bias learning). To address this issue, we propose a Multi-dimensional Adversarial F...
| Authors | Haifeng Zhang, Qinghui He, Xiuli Bi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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