VLA-Mark: A cross modal watermark for large vision-language alignment model

• URL: http://arxiv.org/abs/2507.14067v2
• Date: Fri, 19 Sep 2025 06:54:08 GMT
• Title: VLA-Mark: A cross modal watermark for large vision-language alignment model
• 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,
• Abstract summary: VLA-Mark is a vision-aligned framework that embeds detectable watermarks while preserving semantic fidelity through cross-modal coordination.<n>Our approach integrates multiscale visual-textual alignment metrics, combining localized patch affinity, global semantic coherence, and contextual attention patterns.<n>Experiments show 7.4% lower PPL and 26.6% higher BLEU than conventional methods, with near-perfect detection.
• Score: 44.59029116115437
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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