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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.
This work introduces SVD-MBR, which frames the pairwise utility matrix as a noisy information signal, and reveals that this denoising is metric-dependent: neural metrics encode a robust low-rank consensus ideal for SVD, whereas surface-level metrics struggle to separate signal from metric noise.
This work proposes a framework that efficiently exploits factual information from a knowledge graph via retrieval-augmented generation via retrieval-augmented generation (RAG), with the goal of enabling LVLMs to generate detailed and accurate image explanations.
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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