@inproceedings{liu-etal-2025-vla,
title = "{VLA}-Mark: A cross modal watermark for large vision-language alignment models",
author = "Liu, Shuliang and
Qi, Zheng and
Xu, Jesse Jiaxi and
Yan, Yibo and
Zhang, Junyan and
Geng, He and
Liu, Aiwei and
Jiang, Peijie and
Liu, Jia and
Tam, Yik-Cheung and
Hu, Xuming",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1342/",
pages = "26420--26438",
ISBN = "979-8-89176-332-6",
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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<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.</abstract>
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%0 Conference Proceedings
%T VLA-Mark: A cross modal watermark for large vision-language alignment models
%A Liu, Shuliang
%A Qi, Zheng
%A Xu, Jesse Jiaxi
%A Yan, Yibo
%A Zhang, Junyan
%A Geng, He
%A Liu, Aiwei
%A Jiang, Peijie
%A Liu, Jia
%A Tam, Yik-Cheung
%A Hu, Xuming
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F liu-etal-2025-vla
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1342/
%P 26420-26438
Markdown (Informal)
[VLA-Mark: A cross modal watermark for large vision-language alignment models](https://aclanthology.org/2025.emnlp-main.1342/) (Liu et al., EMNLP 2025)
ACL
- Shuliang Liu, Zheng Qi, Jesse Jiaxi Xu, Yibo Yan, Junyan Zhang, He Geng, Aiwei Liu, Peijie Jiang, Jia Liu, Yik-Cheung Tam, and Xuming Hu. 2025. VLA-Mark: A cross modal watermark for large vision-language alignment models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 26420–26438, Suzhou, China. Association for Computational Linguistics.