AGMark: Attention-Guided Dynamic Watermarking for Large Vision-Language Models

This paper introduces AGMark (Attention-Guided Dynamic Watermarking), a novel watermarking framework for Large Vision-Language Models (LVLMs) that addresses limitations in existing approaches. AGMark dynamically identifies semantic-critical tokens at each decoding step using attention weights and context-aware coherence cues, while determining the proportion of protected tokens through uncertainty awareness and evidence calibration. The framework achieves at least 99.36% detection accuracy (AUC) and maintains robust attack resilience (at least 88.61% AUC) while preserving visual semantic fidelity and generation quality.