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While multimodal large language models (MLLMs) enable a wide range of image-text reasoning tasks, recent incidents indicate that they are vulnerable to illicit deployment and unauthorized distillation. Existing solutions for model provenance are typically confounded by shared language backbones in MLLMs and struggle to...
Dual-stage Native Attribution (DNA) is proposed, a coarse-to-fine framework that follows this hierarchy without additional neural-network training that achieves 89.11% end-to-end attribution accuracy on a task where random guessing accuracy is below 1% and outperforms the strongest baseline by 33.81% even when AEDR is...
FARI is proposed, a one-step inversion framework paired with lightweight adversarial LoRA fine-tuning of the denoiser for watermark extraction that surpasses 50-step DDIM inversion on watermark-verification robustness while dramatically reducing inference time.
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