Beyond 2D Artifacts: Geometric Cues for AI-Generated Image Detection

AI-generated image detection remains challenging under unseen-generator shifts, in-the-wild settings, and repeated online dissemination. Existing image-domain cues can be generator-dependent and can degrade under resizing and recompression during online dissemination. We instead examine geometric coherence, motivated by the fact that real photographs arise from physical 3D scenes whereas AI-generated images need not satisfy the same physical constraints. To capture geometric coherence from a single image, we introduce GeoCue. GeoCue first considers point-normal consistency, which measures the agreement between normals implied by predicted 3D points and independently predicted surface normals. With a frozen geometry predictor, we observe that real photographs exhibit a characteristic range of point-normal consistency, whereas AI-generated images deviate from this range in generator-dependent ways. While point-normal consistency retains its discriminative signal under online dissemination, it can miss geometric changes that preserve point-normal agreement. GeoCue therefore further incorporates reference-relative depth and point deviations. GeoCue consistently outperforms prior methods across unseen-generator and in-the-wild benchmarks. Under repeated online dissemination involving resizing and JPEG/WebP re-encoding, GeoCue retains stable detection performance, while many existing detectors degrade substantially or exhibit unstable performance. These results demonstrate the robustness of GeoCue across generator shifts, in-the-wild data, and repeated online dissemination.