ChromaGuard: Simple Chroma, Strong Generalization for Synthetic Image Detection

Pretrained models have enabled remarkable performance in synthetic image detection by providing rich and transferable visual representations. However, their semantic information can become spuriously associated with forgery labels, causing detectors to rely on semantic shortcuts rather than synthesis artifacts and limiting generalization. To address this challenge, we propose ChromaGuard, a framework for generalizable synthetic image detection that employs chroma guidance to reduce semantic reliance. Through visualization analysis, we reveal that YCbCr chroma is very weakly related to image semantics. Building on this observation, ChromaGuard treats the RGB image and its chroma component as complementary views. The RGB branch preserves the complete image information to avoid missing synthesis artifacts, while the chroma branch guides the detector toward cues weakly associated with semantic content. A simple bidirectional Chroma-RGB interaction module connects the two branches, which alternately injects chroma guidance into RGB features and refines chroma features using the updated RGB representation. To further enhance sensitivity to synthesis artifacts within RBG branch, we introduce a DCT high-frequency adapter to mine frequency-domain artifacts left by generators. Evaluations of cross-generator generalization, semantic sensitivity, and robustness demonstrate its strong performance and reliability, supporting the effectiveness of Chroma guidance.