SAGE: Semantic-Agnostic Image Embedding for Generalized AI-Generated Image Detection

AI-generated image detection has become an important problem in media forensics as modern generative models produce increasingly realistic images. Recent CLIP-based detectors show strong generalization ability, but CLIP image features entangle semantic content with forensic cues. Since semantic content is not an intrinsic generation trace, relying on it can introduce semantic shortcuts and unstable performance under unseen generator shifts. In this paper, we propose SAGE, a Semantic-Agnostic Image Embedding framework for generalized AI-generated image detection. SAGE constructs paired triplets of COCO images, SDXL-reconstructed images, and COCO captions so that real and fake images share the same caption-level semantics. It then suppresses semantic information by suppressing the semantic direction provided by CLIP text embeddings from CLIP image embeddings. To enable caption-free inference, SAGE learns a semantic anchor that approximates the role of paired text embeddings at test time. In addition, SAGE adopts a SoftTriple-style sub-center classifier to model intra-class variation in real and fake images, yielding a more stable authentication geometry across diverse generators. Experiments on CommunityForensics demonstrate that SAGE achieves competitive detection performance while substantially reducing generator-wise performance variation. Further analyses show that SAGE effectively reduces semantic dependence and forms a clearer real/fake feature geometry than strong baselines.