标题： MiraGe：通用AI生成图像检测的多模态判别表示学习 显示英文标题

标题： MiraGe: Multimodal Discriminative Representation Learning for Generalizable AI-Generated Image Detection

摘要： 最近生成模型的进展突显了需要强大检测器，能够区分真实图像和AI生成的图像。 尽管现有方法在已知生成器上表现良好，但当使用新出现或未见过的生成模型进行测试时，其性能通常会下降，这是由于重叠的特征嵌入阻碍了准确的跨生成器分类。 在本文中，我们提出了通用AI生成图像检测的多模态判别表示学习（MiraGe），一种旨在学习生成器不变特征的方法。 受类内变化最小化和类间分离理论见解的启发，MiraGe紧密对齐同一类内的特征，同时最大化类间的分离，从而增强特征判别能力。 此外，我们将多模态提示学习应用于CLIP，进一步将这些原则细化，利用文本嵌入作为语义锚点，以实现有效的判别表示学习，从而提高泛化能力。 在多个基准上的全面实验表明，MiraGe实现了最先进的性能，即使在面对如Sora这样的未见过的生成器时也能保持鲁棒性。 显示英文摘要

摘要： Recent advances in generative models have highlighted the need for robust detectors capable of distinguishing real images from AI-generated images. While existing methods perform well on known generators, their performance often declines when tested with newly emerging or unseen generative models due to overlapping feature embeddings that hinder accurate cross-generator classification. In this paper, we propose Multimodal Discriminative Representation Learning for Generalizable AI-generated Image Detection (MiraGe), a method designed to learn generator-invariant features. Motivated by theoretical insights on intra-class variation minimization and inter-class separation, MiraGe tightly aligns features within the same class while maximizing separation between classes, enhancing feature discriminability. Moreover, we apply multimodal prompt learning to further refine these principles into CLIP, leveraging text embeddings as semantic anchors for effective discriminative representation learning, thereby improving generalizability. Comprehensive experiments across multiple benchmarks show that MiraGe achieves state-of-the-art performance, maintaining robustness even against unseen generators like Sora.