Core Viewpoint - The article discusses the innovative AIGI-Holmes method developed by Xiamen University and Tencent Youtu Lab for detecting AI-generated images, addressing the challenges of interpretability and generalization in existing detection models [2][12][36]. Group 1: Methodology - AIGI-Holmes employs a "large model + visual expert" collaborative architecture to enhance image detection capabilities [2][5]. - The method includes a dual-visual encoder architecture that integrates NPR visual experts to process both high-level semantics and low-level visual features [6]. - The Holmes Pipeline consists of three training phases: visual expert pre-training, supervised fine-tuning (SFT), and direct preference optimization (DPO) [7][22]. Group 2: Key Innovations - The AIGI-Holmes method addresses two critical bottlenecks in existing detection technologies: lack of interpretability and limited generalization capabilities [12][36]. - A new dataset, Holmes-Set, was constructed containing 45,000 images and 20,000 annotations to improve data scarcity issues, covering various types of generation defects [15][18]. - The model architecture includes a collaborative decoding strategy that merges predictions from visual experts and the large language model to enhance detection accuracy [8][25]. Group 3: Performance Evaluation - Experimental results indicate that AIGI-Holmes outperforms existing methods across all benchmarks in detection accuracy and interpretability [10][29]. - The model achieved optimal results in objective metrics (BLEU/ROUGE/METEOR/CIDEr) and subjective evaluations compared to current advanced models [31]. - In robustness tests against common distortions like JPEG compression and Gaussian blur, AIGI-Holmes maintained superior detection accuracy compared to other baseline methods [33][35]. Group 4: Future Directions - The team acknowledges limitations such as the hallucination problem, where the model may misinterpret normal features as defects, and the need for more granular understanding of visual defects [36][39]. - Future work will focus on addressing the hallucination issue, enhancing fine-grained understanding capabilities, and developing objective evaluation metrics for visual defect explanations [39].