UC-VLM: Consistency-Driven Learning for AI-Generated Image Detection with Vision-Language Large Models

Vision-Language Large Models (VLLMs) are promising forAI-generated image (AIGI) detection because they can produce both aprediction and a natural-language output. However, most existing VLLM-based detectors primarily fine-tune the language side while giving lim-ited attention to low-level visual forensic cues. They also often dependon manually crafted prompts or human-annotated rationales, which lim-its scalability. We present UC-VLM, a unified multi-stage frameworkfor AIGI detection that relies solely on binary supervision. UC-VLMfirst identifies effective instruction variants automatically. It then reusesthe same binary label within a multi-stage training framework: (i) a vi-sual discrimination objective that strengthens sensitivity to non-semanticforensic cues, and (ii) a label-conditioned generation objective that usesthe binary label to supervise textual outputs. This design turns weakbinary supervision into a shared supervision signal for both the visualpathway and the language output. Our key novelty is a unified multi-stage binary-supervised framework that consistently reuses the same au-thenticity labels for visual adaptation and label-conditioned text gener-ation, while leveraging automatically optimized instructions to reduceprompt sensitivity without requiring human-written rationales or hand-crafted prompts. Experiments show that UC-VLM achieves 96.1% aver-age accuracy on GenImage, exceeding the strongest prior result by 4.6%,and obtains 69.6% / 77.9% accuracy on Chameleon under ProGAN /SDV1.4 training, surpassing the best baseline by 11.2% / 15.3%, re-spectively.