From Physical Defects to Discriminative Residuals: Generalizable Synthetic Image Detection

Existing synthetic image detectors often exhibit limited generalization across heterogeneous generative architectures due to their reliance on generator-specific artifacts. To address this limitation, we explore physical discrepancies shared across different generative paradigms to identify forensic cues that remain stable across architectures. Through physically motivated probe experiments, we further identify three shared physical defects across GANs, diffusion models, and DiTs, reflected in systematic discrepancies from natural images under resampling operations, spatial smoothing, and high frequency characteristics. Motivated by these observations, we propose a learnable physical prior decoupling framework that transforms such physical defects into discriminative residual representations. Specifically, we introduce three physical prior simulators, Resample, Blur, and Edge, together with learnable residual modulation to adaptively extract complementary residual cues. These residuals are further enhanced in the high-frequency domain and dynamically fused for classification without additional reconstruction supervision. Extensive experiments using only ProGAN generated images for training show that our method consistently outperforms state-of-the-art methods across multiple benchmarks covering different generative architectures and paradigms, demonstrating strong cross architecture generalization.