Generative models have shown a giant leap in synthesizing photo-realistic
images with minimal expertise, sparking concerns about the authenticity of
online information. This study aims to develop a universal AI-generated image
detector capable of identifying images from diverse sources. Existing methods
struggle to generalize across unseen generative models when provided with
limited sample sources. Inspired by the zero-shot transferability of
pre-trained vision-language models, we seek to harness the nontrivial
visual-world knowledge and descriptive proficiency of CLIP-ViT to generalize
over unknown domains. This paper presents a novel parameter-efficient
fine-tuning approach, mixture of low-rank experts, to fully exploit CLIP-ViT's
potential while preserving knowledge and expanding capacity for transferable
detection. We adapt only the MLP layers of deeper ViT blocks via an integration
of shared and separate LoRAs within an MoE-based structure. Extensive
experiments on public benchmarks show that our method achieves superiority over
state-of-the-art approaches in cross-generator generalization and robustness to
perturbations. Remarkably, our best-performing ViT-L/14 variant requires
training only 0.08% of its parameters to surpass the leading baseline by +3.64%
mAP and +12.72% avg.Acc across unseen diffusion and autoregressive models. This
even outperforms the baseline with just 0.28% of the training data. Our code
and pre-trained models will be available at
this https URL

通过开发通用的人工智能生成图像检测器，该研究旨在识别不同来源的图像。该方法通过深入挖掘 CLIP-ViT 的潜力并保留知识，扩展可传递检测的能力，提出了一种新颖的参数高效的微调方法，使用低秩专家的混合模式在 MoE 结构中融合共享和分离的 LoRAs。通过在公共基准测试中进行大量实验，我们的方法在跨生成器泛化和对扰动的鲁棒性方面实现了超越最先进方法的优越性。值得注意的是，我们表现最佳的 ViT-L/14 变体只需训练其 0.08% 的参数即可将最领先的基线提高 + 3.64% 的 mAP 和 + 12.72% 的平均准确率，超越了仅使用训练数据的 0.28% 的基线。