The insights from this work can be extended to improve the performance of other large-scale vision foundation models beyond SAM by applying the adversarial tuning approach to fine-tune these models. The key idea is to generate natural adversarial examples that challenge the model's segmentation capabilities while maintaining photorealism and alignment with original annotations. By leveraging a stable diffusion model to optimize latent representations and incorporating a control branch for precise generation of adversarial samples, the performance of other large-scale vision models can be enhanced. This approach can help address limitations and challenges faced by these models in specific niche applications, similar to the improvements seen in SAM's performance across various segmentation tasks.