Adversarial watermark is an important technique for protecting digital images from unauthorized use and illegal AI training. However, conventional methods often introduce visually unpleasant artifacts, making the watermark easily perceptible. This results in an inherent trade-off between robustness and visual fidelity, where stronger protection comes at the cost of degraded image quality. In this work, we address this challenge by integrating SSIM loss into the perturbation embedding process using the Fully-trained Surrogate Model Guidance (FSMG) from baseline. By employing tunable SSIM weights, our approach balances the adversarial loss—designed to hinder unauthorized model training—with a perceptual loss that preserves image fidelity. Experimental results on CelebA-HQ and VGGFace2 show that our method effectively enhances image quality while preserving robustness, as validated by quantitative metrics and user evaluations confirming its practical viability for content protection.