DeCue: Learning Generator-Invariant Forensic Representations via Feature Decoupling for AI-Generated Image Detection

The rapid growth of image generative models, from GANs to diffusion models, has enabled the widespread creation of photorealistic synthetic images, posing serious threats to digital media authenticity and information integrity. A fundamental bottleneck of AI-generated image detection is the poor cross-generator generalization of existing detectors. We attribute this generalization failure to the inherent entanglement of deep representations. Detectors tend to overfit to superficial semantic content features rather than learning intrinsic generation-invariant forensic traces that transfer across architectures. To address this issue, we present DeCue, a novel feature decoupling framework that explicitly disentangles entangled representations into forensic cue features encoding generation-specific discriminative artifacts, and content features that capture image semantic information. The proposed DeCue achieves robust feature decoupling through hard fake sample construction and cross-reconstruction constraint. The hard fake samples are strategically synthesized to eliminate trivial content-level disparities between real and fake images, compelling the model to learn generalizable forensic cues instead of relying on content shortcuts. The cross-reconstruction constraint further provides explicit supervisory signals to guaranty effective feature decoupling. Extensive evaluations across 8 benchmarks, including 5 standard academic benchmarks and 3 challenging in-the-wild benchmarks, show that our DeCue achieves a mean accuracy of \textbf{99.23\\%}, substantially outperforming the state-of-the-art detector by \textbf{11.29\\%}, demonstrating its strong robustness and superior cross-generator generalization capability in practical forensic scenarios.