CLOVER: Coverage-Led Optimization of Verifiable Evidence Reasoning for AI-Generated Image Detection

AI-generated image detectors must recognize forgeries across diverse generators and scenes and provide explanations. Binary labels are inexpensive to obtain but provide no supervision for explanations, whereas evidence annotations are costly and may still omit valid observations. To this end, we introduce CLOVER, a training framework that expands detection coverage through binary supervision and advances explanation learning through open-ended evidence verification. For coverage-led supervised fine-tuning, we combine a large binary-labeled image collection for detection with a smaller, scene-diverse evidence corpus for explanation learning. This allows new forgery sources to enter training at low annotation cost with only binary labels. Atomic evidence reinforcement learning then improves evidence quality in two stages: first encouraging the discovery of more visually supported observations, then strengthening penalties for unsupported or uncertain claims. During this process, we employ a self-judge to assess the model's open-ended evidence against the image one claim at a time, enabling rewards that require authenticity labels but no reference explanations. Experiments show that supervised training already achieves strong detection and explanation performance with an evidence corpus roughly 10% the size of the binary corpus. Subsequent evidence optimization further improves precision and reduces hallucinations, demonstrating that visual verification can improve explanation reliability without collecting additional reference explanations.