The proposed visualization and threshold optimization methods can be applied to a wide range of data or domain shifts beyond the Face Anti-Spoofing (FAS) use case. For instance, in medical imaging tasks, where different imaging modalities or patient populations may introduce domain shifts, visualizing the data domain changes and setting thresholds based on the distribution of training data can help improve the model's performance in detecting diseases or abnormalities. In autonomous driving scenarios, where environmental conditions vary, the methods can be used to analyze how the model responds to different driving scenarios and optimize thresholds for decision-making. The results may differ based on the specific task; for example, in medical imaging, data augmentation techniques like contrast adjustment or noise addition may be more relevant, while in autonomous driving, augmentation methods focused on simulating weather conditions or lighting variations could be more beneficial. Overall, the proposed methods offer a versatile approach to addressing data domain shifts and optimizing model performance across various computer vision tasks.