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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.14067 |
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| _version_ | 1866908546284650496 |
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| author | Liu, Shuliang Zheng, Qi Xu, Jesse Jiaxi Yan, Yibo Zhang, Junyan Geng, He Liu, Aiwei Jiang, Peijie Liu, Jia Tam, Yik-Cheung Hu, Xuming |
| author_facet | Liu, Shuliang Zheng, Qi Xu, Jesse Jiaxi Yan, Yibo Zhang, Junyan Geng, He Liu, Aiwei Jiang, Peijie Liu, Jia Tam, Yik-Cheung Hu, Xuming |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14067 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VLA-Mark: A cross modal watermark for large vision-language alignment model Liu, Shuliang Zheng, Qi Xu, Jesse Jiaxi Yan, Yibo Zhang, Junyan Geng, He Liu, Aiwei Jiang, Peijie Liu, Jia Tam, Yik-Cheung Hu, Xuming Computer Vision and Pattern Recognition Artificial Intelligence 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 |
| title | VLA-Mark: A cross modal watermark for large vision-language alignment model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.14067 |