High-visibility watermarks with strong visual saliency are particularly vulnerable to removal by deep learning models. Existing adversarial-perturbation-based protection methods for visible watermarks often suffer from poor perturbation stability and significant image quality degradation in high-visibility scenarios. To address these challenges, the authors propose a cascaded coarse-to-fine framework that generates an adversarial perturbation High-visibility Watermark Vaccine (HWV), specifically aimed at protecting high-visibility watermarks. They establish a watermark-driven perturbation generation model and design cascaded loss functions for coarse and fine stages to guide the multi-phase search for a globally optimal adversarial solution. In the coarse stage, a composite loss function is constructed to achieve robust protection of high-visibility watermarks while in the fine stage, a perturbation minimization objective is introduced to mitigate the impact of perturbations on image quality. Moreover, the authors propose a novel gradient normalization equation combined with a dynamic momentum update strategy to adaptively optimize the perturbation step size, accelerating convergence toward the global optimum of the loss function. Experimental results on the CLWD dataset demonstrate that the proposed method effectively prevents removal attacks targeting high-visibility watermarked images. Furthermore, compared to conventional single-stage loss methods, this method significantly improves the image quality of perturbed watermark images, achieving a Peak Signal-to-Noise Ratio greater than 44 dB. This work provides a novel perspective for enhancing digital image copyright protection against deep-learning-based attacks.