## 4 Results
### Pre-Trained Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Trained on GB and**} & \multicolumn{3}{c}{**Trained on DL and**} & \multicolumn{3}{c}{**Trained on GB and**} & \multicolumn{3}{c}{**Trained on DL**} \\ \multicolumn{2}{c}{**Tested on GB CC**} & \multicolumn{3}{c}{**Tested on GB CC**} & \multicolumn{3}{c}{**Tested on GB SS**} & \multicolumn{3}{c}{**and Tested on GB SS**} \\ \cline{2-13} \multicolumn{1}{c}{**Alg.**} & **BPCER** & **APCER** & **Liv.** & **BPCER** & **APCER** & **Liv.** & **BPCER** & **APCER** & **Liv.** & **BPCER** & **APCER** & **Liv.** \\  & [75] & [74] & **Acc.15** & [75] & [74] & **Acc.15** & [75] & [74] & **Acc.15** & [75] & [74] & **Acc.15** \\ \hline Col (\") & 0.20 & 29.88 & 83.61 & 0.24 & 23.78 & 86.92 & 0.20 & 24.76 & 86.41 & 0.24 & 21.38 & 88.23 \\ \hline Dob (\") & 0.59 & 25.41 & 85.88 & 0.44 & 29.54 & 83.53 & 0.59 & 3.25 & 97.96 & 0.44 & 4.72 & 97.23 \\ \hline con & 8.98 & 3.94 & 93.77 & 1.85 & 80.69 & 55.14 & 8.98 & 26.67 & 81.37 & 1.85 & 94.55 & 47.58 \\ \hline JLW & 2.59 & 8.21 & 94.35 & 0.20 & 87.76 & 52.04 & 2.59 & 54.11 & 69.31 & 0.20 & 79.76 & 56.41 \\ \hline m\_s (\") & 0.29 & 6.30 & 96.43 & 0.29 & 6.30 & 96.43 & 0.29 & 13.94 & 92.26 & 0.29 & 13.95 & 92.26 \\ \hline m\_e (\") & 0.05 & 2.72 & 98.49 & 0.05 & 2.72 & 98.49 & 0.05 & 13.62 & 92.55 & 0.15 & 13.62 & 92.55 \\ \hline PAD (\") & 1.46 & 37.20 & 79.05 & 1.46 & 37.20 & 79.05 & 1.46 & 18.42 & 89.29 & 89.29 & 1.46 & 18.42 & 89.29 \\ \hline Bld & 3.61 & 5.37 & 95.43 & 6.49 & 86.18 & 50.04 & 3.61 & 27.56 & 83.32 & 6.49 & 89.47 & 48.25 \\ \hline bbs & 3.46 & 7.85 & 94.15 & 2.29 & 98.25 & 45.37 & 3.46 & 39.8 & 76.72 & 2.29 & 91.54 & 49.02 \\ \hline zd2 & 2.20 & 12.36 & 92.26 & 1.66 & 96.34 & 46.70 & 2.20 & 57.93 & 67.06 & 1.66 & 89.02 & 50.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of LivDet 2021 competitors on test sets acquired with the GreenBit sensor. In particular, GB CC is a test set acquired with the consensual technique, while GB SS is a test set acquired with the ScreenSpoof technique. (*) indicates training on both GreenBit and Dermalog Livdet training sets. (*) indicates training on additional data with respect to LivDet training sets. To control the experiment more and to select data that were unknown during the training phase, we submitted images to the LivDet 2021 PADs acquired with two very different sensors compared to those used during the eighth edition of the competition, LivDet Orchantus 2019 and LivDet Biometrika 2013. The results of this analysis are shown in Table 7. In this case, some data are unavailable, so we have to restrict the analysis to six PADs. Apart from a few exceptions with _megvii_, all PADs show a significant drop in performance and are completely ineffective on the new data. This is evident, above all, from the results on Orchantus 2019, whose non-optical acquisition technology obtains very different images compared to the sensors of the training set. It is worth underlining that, based on the type of image, the error can be shifted entirely to fakes, as in the case of Biometrika 2013, or entirely to live, as in the case of Orcanthus 2019. From these results, we can hypothesize that training on different types of data improves the performance on unknown attacks such as ScreenSpoof at the expense of a greater intra-sensor APCER. This is evident, for example, from _LivDet_Dob_C2_ in Table 5, which, in the intra-method test (i.e., trained on the GreenBit train and tested on the consensual GreenBit test (GB CC), is among the least accurate PADs with 85.88% accuracy, while in the cross-method test (i.e., trained on the GreenBit train and tested on the GreenBit ScreenSpoof (GB SS) test), it is the highest performing, exceeding 97% accuracy. However, the lack of controllability of the training phase is a limitation of this analysis. For this reason, in the next section, we analyzed the behavior of completely self-trained PADs where both the implementation and the training data were known.