通过分析生成图像的频域特征，实现跨模型AIGC检测
S^2F-Net:A Robust Spatial-Spectral Fusion Framework for Cross-Model AIGC Detection
- 利用频域中特有的上采样痕迹作为检测信号
- 在17类生成模型上达到90.49%的检测准确率
- 适合需要泛化能力的AI内容安全检测场景
生成模型的快速发展对具备强泛化能力的检测方法提出了迫切需求。然而，现有检测方法通常对特定源模型过拟合，在面对未见生成架构时性能显著下降。为此，本文提出一种名为S²F-Net的跨模型检测框架，其核心在于挖掘并利用真实与合成纹理间的内在频谱差异。考虑到上采样操作会在纹理贫乏和丰富区域留下独特且可区分的频率指纹，本研究聚焦于频域伪影的检测，旨在从根本上提升模型的泛化性能。具体而言，我们引入了一个可学习的频域注意力模块，通过融合空间纹理分析与频谱依赖关系，自适应地加权并增强具有判别性的频段。在包含17类生成模型的AIGCDetectBenchmark上，S²F-Net实现了90.49%的检测准确率，显著优于多种现有基线方法，在跨域检测场景中表现突出。
原文摘要 · Abstract (English)
The rapid development of generative models has imposed an urgent demand for detection schemes with strong generalization capabilities. However, existing detection methods generally suffer from overfitting to specific source models, leading to significant performance degradation when confronted with unseen generative architectures. To address these challenges, this paper proposes a cross-model detection framework called S 2 F-Net, whose core lies in exploring and leveraging the inherent spectral discrepancies between real and synthetic textures. Considering that upsampling operations leave unique and distinguishable frequency fingerprints in both texture-poor and texture-rich regions, we focus our research on the detection of frequency-domain artifacts, aiming to fundamentally improve the generalization performance of the model. Specifically, we introduce a learnable frequency attention module that adaptively weights and enhances discriminative frequency bands by synergizing spatial texture analysis and spectral dependencies.On the AIGCDetectBenchmark, which includes 17 categories of generative models, S 2 F-Net achieves a detection accuracy of 90.49%, significantly outperforming various existing baseline methods in cross-domain detection scenarios.
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