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In conclusion, the proposed GIDLMM framework successfully utilizes Large Multimodal Models to enhance AI-generated image detection. The system achieved an average accuracy of 89% and an AUC of 95% while training with only 1,000 samples and modifying less than 0.34% of the parameters . The proposed "AI Judges" mechanism proved to be both efficient and effective. Furthermore, the model outperforms state-of-the-art approaches on commercial datasets by over 13% and uniquely provides interpretable explanations for decisions, addressing the critical need for trust in AI detection |
IEEE/ICACT20260070 Slide.23
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