{"ID":2893184,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.14067","arxiv_id":"2507.14067","title":"VLA-Mark: A cross modal watermark for large vision-language alignment model","abstract":"Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual-textual alignment through biased token selection and static strategies, leaving semantic-critical concepts vulnerable. We propose VLA-Mark, a vision-aligned framework that embeds detectable watermarks while preserving semantic fidelity through cross-modal coordination. Our approach integrates multiscale visual-textual alignment metrics, combining localized patch affinity, global semantic coherence, and contextual attention patterns, to guide watermark injection without model retraining. An entropy-sensitive mechanism dynamically balances watermark strength and semantic preservation, prioritizing visual grounding during low-uncertainty generation phases. Experiments show 7.4% lower PPL and 26.6% higher BLEU than conventional methods, with near-perfect detection (98.8% AUC). The framework demonstrates 96.1\\% attack resilience against attacks such as paraphrasing and synonym substitution, while maintaining text-visual consistency, establishing new standards for quality-preserving multimodal watermarking","short_abstract":"Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual-textual alignment through biased token selection and static strategies, leaving semantic-critical concepts vulnerable. We propose VLA-Mark,...","url_abs":"https://arxiv.org/abs/2507.14067","url_pdf":"https://arxiv.org/pdf/2507.14067v2","authors":"[\"Shuliang Liu\",\"Qi Zheng\",\"Jesse Jiaxi Xu\",\"Yibo Yan\",\"Junyan Zhang\",\"He Geng\",\"Aiwei Liu\",\"Peijie Jiang\",\"Jia Liu\",\"Yik-Cheung Tam\",\"Xuming Hu\"]","published":"2025-07-18T16:44:41Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}