Mixture of Low-rank Experts for Transferable AI-Generated Image Detection

• URL: http://arxiv.org/abs/2404.04883v1
• Date: Sun, 7 Apr 2024 09:01:50 GMT
• Title: Mixture of Low-rank Experts for Transferable AI-Generated Image Detection
• Authors: Zihan Liu, Hanyi Wang, Yaoyu Kang, Shilin Wang,
• Abstract summary: Generative models have shown a giant leap in 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. Inspired by the zero-shot transferability of pre-trained vision-language models, we seek to harness the non-trivial visual-world knowledge and descriptive proficiency of CLIP-ViT to generalize over unknown domains.
• Score: 18.631006488565664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://github.com/zhliuworks/CLIPMoLE.

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