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IEEE/ICACT20260070 Slide.04        [Big Slide]	Oral Presentation
	This study presents several key contributions. First, the GIDLMM framework is introduced, combining a detection module based on CLIP and LoRA with an interpretation module that explains visual artifacts . Second, the method demonstrates high efficiency, achieving effective detection using only 1,000 training samples and fine-tuning a small fraction of parameters. Third, a novel "AI Judges" prompt mechanism is proposed to select optimal image descriptions without the high computational cost of traditional methods, improving both quality and efficiency for real-world applications .

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