融合频谱与语义特征，提升生成图像检测鲁棒性
FUSE: Unifying Spectral and Semantic Cues for Robust AI-Generated Image Detection
- 用傅里叶变换提取频谱特征，结合CLIP视觉编码器获取语义特征
- 在GenImage等5个数据集上平均准确率达91.36%，Chameleon上达94.96%均值AP
- 对高保真图像仍保持稳定检测能力，适合多生成器场景应用
生成模型的快速演进加剧了对可信AI生成图像检测的需求。为此，我们提出FUSE，一种融合快速傅里叶变换提取的频谱特征与CLIP视觉编码器获得的语义特征的混合系统。特征被融合为联合表示，并采用两阶段渐进式训练。在GenImage、WildFake、DiTFake、GPT-ImgEval和Chameleon数据集上的评估表明，该方法在多种生成器间具有强泛化能力。FUSE（第一阶段）在Chameleon基准上达到最先进性能，于GenImage数据集上实现91.36%平均准确率，所有测试生成器上平均准确率为88.71%，平均精度均值达94.96%。第二阶段训练进一步提升了多数生成器的性能。不同于现有方法在Chameleon高保真图像上表现不佳，本方法在多种生成器下均保持鲁棒性。结果凸显了融合频谱与语义特征在通用生成图像检测中的优势。
原文摘要 · Abstract (English)
The fast evolution of generative models has heightened the demand for reliable detection of AI-generated images. To tackle this challenge, we introduce FUSE, a hybrid system that combines spectral features extracted through Fast Fourier Transform with semantic features obtained from the CLIP's Vision encoder. The features are fused into a joint representation and trained progressively in two stages. Evaluations on GenImage, WildFake, DiTFake, GPT-ImgEval and Chameleon datasets demonstrate strong generalization across multiple generators. Our FUSE (Stage 1) model demonstrates state-of-the-art results on the Chameleon benchmark. It also attains 91.36% mean accuracy on the GenImage dataset, 88.71% accuracy across all tested generators, and a mean Average Precision of 94.96%. Stage 2 training further improves performance for most generators. Unlike existing methods, which often perform poorly on high-fidelity images in Chameleon, our approach maintains robustness across diverse generators. These findings highlight the benefits of integrating spectral and semantic features for generalized detection of images generated by AI.
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