仅用极少样本即可精准识别多种AI生成图像，解决真实检测数据稀缺难题。
FAMSeC: A Few-shot-sample-based General AI-generated Image Detection Method
- 基于LoRA的伪造感知模块，从少量样本中高效学习
- 使用语义引导对比学习，提升真实与伪造图像区分能力
- 仅需0.56%训练数据就超越现有方法14.55%准确率
生成式AI的爆发式增长使网络充斥着各类AI生成图像，引发安全担忧，亟需可靠的检测手段。现有方法依赖大量来自不同模型的假图进行训练以实现泛化，但受限于闭源模型和访问权限，实际可用样本稀少。因此，如何在少样本条件下构建通用检测器至关重要。为此，我们提出FAMSeC，一种基于LoRA的伪造感知模块（FAM）与语义特征引导对比学习策略（SeC）的通用检测方法。FAM利用LoRA结构，在有限样本下保持预训练特征的泛化能力，防止过拟合；SeC则引导模型关注真实与伪造图像间的差异，而非样本自身特征。实验表明，FAMSeC仅使用0.56%的训练样本，分类准确率相比最先进方法提升14.55%。
原文摘要 · Abstract (English)
The explosive growth of generative AI has saturated the internet with AI-generated images, raising security concerns and increasing the need for reliable detection methods. The primary requirement for such detection is generalizability, typically achieved by training on numerous fake images from various models. However, practical limitations, such as closed-source models and restricted access, often result in limited training samples. Therefore, training a general detector with few-shot samples is essential for modern detection mechanisms. To address this challenge, we propose FAMSeC, a general AI-generated image detection method based on LoRA-based Forgery Awareness Module and Semantic feature-guided Contrastive learning strategy. To effectively learn from limited samples and prevent overfitting, we developed a Forgery Awareness Module (FAM) based on LoRA, maintaining the generalization of pre-trained features. Additionally, to cooperate with FAM, we designed a Semantic feature-guided Contrastive learning strategy (SeC), making the FAM focus more on the differences between real/fake image than on the features of the samples themselves. Experiments show that FAMSeC outperforms state-of-the-art method, enhancing classification accuracy by 14.55% with just 0.56% of the training samples.
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