## 5 Conclusions  
Due to the unique structural and pixel features of sub-dermal fingertip tissue, if all the effective regions in the OCT image area are simply used for overall feature extraction, important and secondary features will often be treated equally, which has an adverse impact on anti-spoofing performance. Therefore, this paper proposes an anti-spoofing method based on the contribution of sub-dermal tissue structure at the fingertips. Firstly, it analyzes the relationship between structure and pixels as well as existing problems during MAE reconstruction. Then, through ablation experiments on different fingertip tissues using the Shapley value, the quantitative contribution of different tissues to anti-spoofing are  
Figure 14: Anti-spoofing performance and visual representations of different out-of-distribution spoof materials.  
obtained and transformed into weights for anti-spoofing tasks. Finally, by combining the variational structure with the attention mechanism and using the high-dimensional fused data distribution as the criterion for anomaly detection, the anti-spoofing performance of the model is effectively improved.  
This study investigated unsupervised anti-spoofing methods of OCT images based on fingertip tissue structures. Although it achieved good results in terms of test performance on existing fingerprint datasets, in-depth research is still needed concerning the following aspects:  
1. This study explored the impact of the sub-dermal tissue structure segmentation task and the semantic feature reconstruction task on anti-spoofing performance from the perspective of correlation. In the future, tasks related to speckle noise suppression and image style learning can be introduced to obtain more efficient OCT fingerprint feature representations and establish a correlation analysis standard.
2. The experimental data in this study were trained and tested within a single domain, and research and discussion on cross-domain datasets was not included. Future research can explore the task correlation between data from different domains through transfer learning methods to further improve the generalization of the method.  
In the future, OCT fingerprint data from diverse populations and devices will be collected to expand the coverage of spoof samples and enhance the model's domain generalization and adaptability.  
Conceptualization, Z.L. and Y.L.; data curation, J.Q.; formal analysis, J.J.; funding acquisition, Y.L.; methodology, Z.L. and H.F.; project administration, Y.L.; resources, J.Q.; software, Z.L. and J.Q.; supervision, Y.L.; validation, J.J. and H.F.; visualization, J.J. and H.F.; writing--original draft, Z.L., J.J., J.Q., H.F. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (62376251, 62076220) and the Medical Health Science and Technology Project of Zhejiang Province (2024 KY881).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We would like to express our deepest gratitude to all those who have contributed to the completion of this research and the writing of this paper.  
The authors declare no conflicts of interest.