# Modal-Guided Multi-Domain Inconsistency Learning for Face Forgery Detection
## 4 Experiments
### Comparisons with SOTA Methods  
In this section, we report the performance of the proposed method against that of existing state-of-the-art methods on the DFDC and DFT datasets. The Area Under the Receiver Operating Characteristic Curve (AUC) and accuracy (Acc) are adopted as the evaluation metrics. Since the training and test data from DFDC employed with the other SOTA methods are unknown, to ensure a fair comparison, we selected the samples randomly when constructing the multi-modal dataset and evaluated the SOTA methods on it. Table 3 presents the comparison of our method against the SOTA methods on the DFDC, DFT-HQ, and DFT-LQ datasets. The performance is evaluated according to the AUC and Acc (indicates \"Accuracy on DFDC\"). We can observe that our method achieves an AUC of 98.1% on the DFDC dataset, which outperforms the existing approaches by a considerable margin. For example, the visual-modality-based method Xception achieves an AUC of 87.2%, and ours exceeds it by a large gain of 10.9%. Moreover, compared with Meso4, F3-Net, and Multi-Att, the proposed method beats them by a margin of 17.8%, 2.9%, and 2.5%, respectively. Equally, we also employed the evaluation metric of Acc on the DFDC dataset, and our method acquired an accuracy of 93.3%, outperforming the second-best Multi-Att by a gain of 2.8%. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Modalities**} & \multicolumn{1}{c}{**Label**} \\ \cline{2-4}  & **Audio** & **Visual** & **Number** \\ \hline CelebDF [10] & \(\times\) & ✓ & 1 \\ DFT [11] & ✓ & ✓ & 1 \\ UADFV [12] & \(\times\) & ✓ & 1 \\ FF++ [13] & \(\times\) & ✓ & 1 \\ WildDeepfake [37] & \(\times\) & ✓ & 1 \\ FakeAVCeleb [36] & ✓ & ✓ & 3 \\ DFDC [38] & ✓ & ✓ & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The specifics of existing available deepfake detection datasets. Figure 4: Visualizations of our constructed dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & **Label** & **Real** & \multicolumn{3}{c}{**Fake**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-7}  & **V-A** & **R-R** & **R-F** & **F-R** & **F-F** & \\ \hline DFDC & ✓ & 6584 & 2160 & 2163 & 2193 & 13,100 \\ DFT-HQ & ✓ & 309 & 309 & 316 & 316 & 1250 \\ DFT-LQ & ✓ & 316 & 316 & 316 & 316 & 1264 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The details of the reproduced multi-modal deepfake detection datasets. Note that \"R-F\" is the visual–audio modal label which equals ”Real–Fake”. To further reveal the performance of the proposed method, we report the performance of the SOTA methods evaluated on the DFT-LQ, and DFT-HQ datasets in Table 3. Intriguingly, our approach consistently occupies first place ahead of the other counterparts, which demonstrates the superiority of our MGDL-Net. To evaluate the effectiveness of our method, we also conduct experiments on the DFT-TIMIT dataset. It is compared with existing state-of-the-art methods in two resolutions (LQ and HQ), as reflected in Table 3. Evaluated according to the AUC score, our method achieves 99.8% on the LQ version and 99.6% on the HQ version of the DFT dataset, ranking first among all of the methods. It is worth noting that even though the data are unevenly distributed during training, our method consistently outperforms the other state-of-the-art methods on both DFT-LQ and DFT-HQ. One possible reason for our method outperforming the other methods on the DFDC and DFT datasets is the learning of heterogenous features from the audio-visual dataset with multi-labels. Our framework enables fine-grained optimization on each individual modality during joint training. Equally, the discriminative inconsistency features learned spatiotemporally and across modalities form a complementary relationship, which prompts the proposed framework to provide each unimodality with the information overlooked.