Effort

Social Gaze Consistency as a Semantic Cue for AI-Generated Image Detection

This paper introduces Social Gaze Consistency (SGC), a high-level semantic detection axis based on the mutual coherence of gaze direction, head-eye alignment, and pupil placement between interacting individuals in images. The authors construct a controlled diagnostic dataset with region-specific gaze perturbations and a Block-Compositional Caption Supervision scheme to train detectors without generator-fingerprint memorization shortcuts. Cross-architecture validation shows +3.7 pp improvement on the COCOAI Interaction subset when applied to FakeVLM, with gains transferring from a single inpainter (FLUX.1-Fill) to multi-generator suites. The work argues that diffusion models share a spectral weakness in periocular structure, making gaze coherence a robust, backbone-agnostic detection signal orthogonal to existing low-level artifact methods.