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Experimental results and analyses show that IMFD improves multi-face forgery detection by integrating face bounding boxes into the instruction, and consistently outperforms various state-of-the-art methods.
TripPattern is a watermarking framework that formulates text watermarking as a pattern-based matching task using three vocabulary partitions, which maintains LLM generation quality while achieving robust watermark detectability.
A scalable evaluation framework for simile understanding is proposed that includes a controlled simile dataset in which metaphorical vehicles are drawn from a predefined set of object-detectable categories and combined with diverse templates.
This work finds that models cannot reliably reconstruct the original meaning and layer-wise probe classifiers fail to detect the harmful intent of perturbed prompts, and perturbations can occasionally reduce attack success by inducing off-topic or incoherent responses.
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