# Modal-Guided Multi-Domain Inconsistency Learning for Face Forgery Detection
## 4 Experiments
### Impacts of the Compression Rate
The results are summarized in Table 6, where HQ\(\rightarrow\)LQ indicates the proposed method is trained on HQ data and tested on LQ data. We can observe the following from the results:  
(1) For spatial inconsistency, the model with the setting of \"LQ\(\rightarrow\)HQ\" leads to a 23.95% performance degradation, while \"HQ\(\rightarrow\)LQ\"causes a reduction of 1.28%. One potential reason for this is that artifacts in the images are less noticeable due to the data compression. (2) Compared with spatial inconsistency alone, the performance reduction for the spatiotemporal inconsistency model with settings of both \"LQ\(\rightarrow\)HQ\" and \"HQ\(\rightarrow\)LQ\" is reduced to 15.38% and 0.05%, which is significantly improved. This demonstrates the effectiveness of introducing temporal inconsistencies into the detection model. (3) Finally, we leverage the modality features to address spatiotemporal inconsistencies, with almost no decline in accuracy observed for both the 'LQ\(\rightarrow\)HQ' and 'HQ\(\rightarrow\)LQ' settings. One possible reason for this is that visual artifacts may become less noticeable with higher \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Resolution**} & \multirow{2}{*}{**Acc**} & \multicolumn{1}{c}{**Acc**} \\ \cline{2-3}  & **Train** & **Test** & & **Reduction** \\ \hline \multirow{4}{*}{Spatial} & LQ & LQ & 99.75 & 23.95 \\  & LQ & HQ & 75.80 & 23.95 \\ \cline{2-3}  & HQ & HQ & 99.67 & \\  & HQ & LQ & 98.39 & 1.28 \\ \hline \multirow{4}{*}{Spatial + Temporal} & LQ & LQ & 99.78 & 15.38 \\  & LQ & HQ & 84.40 & 15.38 \\ \cline{1-1} \cline{2-3}  & HQ & HQ & 99.81 & \\ \cline{1-1} \cline{2-3}  & HQ & LQ & 99.76 & 0.05 \\ \hline \multirow{4}{*}{Spatial + Temporal + Frequency} & LQ & LQ & 99.90 & \\ \cline{1-1} \cline{2-3}  & LQ & HQ & 99.88 & 0.02 \\ \cline{1-1} \cline{2-3}  & HQ & HQ & 99.82 & 0.05 \\ \cline{1-1} \cline{2-3}  & HQ & LQ & 99.77 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of accuracy variations caused by compressibility on the DFT-LQ and DFT-HQ datasets. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Deepfakes**} & \multicolumn{2}{c}{**Face2 Face**} & \multicolumn{2}{c}{**FaceSwap**} & \multicolumn{2}{c}{**NeuralTextures**} & \multicolumn{2}{c}{**Celeb-DF**} \\ \cline{2-11}  & **C23-C40** & **C40-C23** & **C23-C40** & **C40-C23** & **C23-C40** & **C40-C23** & **C23-C40** & **C40-C23** & **C23-C40** & **C40-C23** \\ \hline CFUC [42] & 67.75 & 79.27 & 54.50 & 73.90 & 55.75 & 80.00 & 53.75 & 63.66 & 50.58 & 69.89 \\ MesoNet [39] & 78.75 & 78.76 & 59.75 & 60.36 & 69.75 & 71.03 & 53.25 & 60.05 & 57.25 & 66.70 \\ DDRN [48] & 71.93 & 76.15 & 52.01 & 54.08 & 50.95 & 51.98 & 51.76 & 58.21 & 51.70 & 66.07 \\ QVAR [49] & 57.04 & 81.71 & 50.00 & 58.97 & 56.82 & 54.02 & 60.57 & 52.50 & 72.63 & 62.54 \\ LSF [50] & 79.55 & 83.66 & 54.52 & 75.06 & 69.10 & 78.11 & 59.80 & 64.63 & 64.55 & 79.17 \\ FTSC [51] & 88.78 & 84.20 & 77.66 & 84.19 & 79.95 & 86.75 & 77.78 & 80.52 & 79.89 & 80.96 \\ DSTC [41] & 90.75 & 93.62 & 83.95 & 89.06 & 86.13 & 87.92 & 80.24 & 84.78 & 80.29 & 82.04 \\ \hline Ours & 95.28 & 97.17 & 88.07 & 92.62 & 90.11 & 93.70 & 83.12 & 89.21 & 85.33 & 86.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of cross-compression detection evaluated using the AUC with that of the state-of-the-art methods on FF++ [13]. data compression, but the modality inconsistency between fake images and audio is not significantly affected. In other words, although the discriminative ability of the visually inconsistent features encoded by the model may be weakened due to high data compression, the cross-modal inconsistency is not impaired, and it can play a more important role in deepfake detection.