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On datasets from commercial tools such as Midjourney, Adobe Firefly, and DALL-E 3, GIDLMM demonstrates a distinct advantage. Even when trained on just 1,000 images, the model achieves an average accuracy of 94.87%, significantly outperforming the CLIPping model, which only reached 89.63% even with 100,000 training samples. This represents a performance improvement of nearly 14%. These results demonstrate that GIDLMM is exceptionally effective at detecting high-quality images from modern commercial generators without requiring massive training datasets. |
IEEE/ICACT20260070 Slide.19
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