## 3 Proposed Method
### Ablation Experiment on Tissue Texture of Bona Fide and Spoof Samples  
It is inferred from the MAE masking experiment that both structural features and pixel features have an impact on the fingerprint reconstruction results. However, the contribution of each part still cannot be determined. In this subsection, the methods of mixing and ablation of the texture structures of bona fide and spoof samples are adopted to clarify the specific contributions of each tissue structure in terms of anti-spoofing. Since the supervised method has a high degree of accuracy and can effectively reveal the internal characteristics of the data through bona fide and spoof labels, the supervised method is combined for the ablation experiment. The core of the experimental design lies in gradually removing or adding specific tissue structures to observe their impacts on the final anti-spoofing effect. Given that the high accuracy rate of supervised anti-spoofing methods can often reach 100%, this paper adopts a binary classification network constructed by ordinary convolutional and fully connected layer modules as the baseline network for pre-training. The experimental training set uses 5000 bona fide samples and 5000 spoof samples for pre-training. For the test set, this paper randomly selects 100 bona fide samples after tissue segmentation and fuses them with 2000 spoof samples. Moreover, the segmented tissue structures are randomly selected from the 100 bona fide fingerprints and integrated into the 2000 spoof sample images. The fusion results are shown in Figure 9. During the entire training process, the training epoch is set to 100, the batchsize of images is 15, the learning rate is 0.001, the optimizer is Adam, and the experimental results are shown in Table 2.  
In the table, the anti-spoofing accuracy is the correct rate of the model in judging authenticity after fusing the labels. It can be seen that different regions contribute differently to the anti-spoofing method. Compared with the stratum corneum and the glass layer, which can only provide partial anti-spoofing contributions, the dermal tissue often provides more information. In order to convert the experimental results into quantifiable weights for the model, this subsection proposes using the Shapley value to calculate the contribution weights and thereby determine the impact of different tissue regions on the anti-spoofing results. Figure 9: Ablation study on tissue texture. (**a**) Fuse the labels of the viable epidermis layer. (**b**) Fuse the labels of the glass layer. (**c**) Fuse the labels of the stratum corneum. The Shapley value is used in game theory to distribute profits or costs, and it was proposed by the American economist [PERSON] in 1953. The Shapley value is used in evaluating the contribution of each participant to the overall outcome and distributing profits or costs on this basis [47]. For a multi-person cooperative project, the Shapley value measures the average contribution of each participant in all possible combinations, as well as the marginal contribution when joining or withdrawing from the cooperation. This is obtained by considering all possible permutations and combinations of participants and calculating the impact of each participant's joining or withdrawing on the final result. Suppose there are \(n\) key regional features \(\tau=(\tau_{1},\tau_{2},\tau_{3},\ldots,\tau_{n})\) in the anti-spoofing task, and its Shapley value is obtained through Equation (4):  
\[S_{i}(v)=\sum_{S\subseteq N,i\in S}\frac{(|S|-1)!(n-|S|)!}{n!}[v(S)-v(S-\{i\})] \tag{4}\]  
where \(S_{i}(v)\) is the Shapley value of the fingertip tissue, \(N\) represents the set of all tissue features, \(S\) is a subset of the tissue features, and \(v(S)\) represents the value provided in the cooperation, that is, the accuracy obtained in Table 2. Finally, in this paper, the proportion of the contribution of each key feature is roughly the following: viable epidermis layer:glass layer:stratum corneum = 4:1:0.2.