# GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
## 5 Future Work  
This section discusses strategies for future research tackling our proposed model's limitations (as discussed in Section 4.4). In essence, although our proposed deepfake detection model's performance is high for some datasets, the main limitation is that its performance is lower for some datasets that were seen during training (e.g., Celeb-DF (v2), as discussed in Section 4.4.1), and other datasets that were not seen during training (i.e., that are out of distribution, demonstrating its limited generalization, as discussed in Section 4.4.2).  
To tackle these limitations, the deepfake classification model could incorporate additional features that allow it to generalize better. For example, a recent effective approach to synthetic image detection is using latent representations from pretrained foundation models such as CLIP [85].  
Still, even with more generalizable features, deepfake detection models will continue to struggle with OOD samples. Therefore, another viable strategy would be to perform out-of-distribution detection prior to deepfake classification [86]. This way, we would know when the model's output cannot be trusted due to the input being OOD.  
Finally, another strategy is to perform one-class training for deepfake detection. That is, by only considering real samples during training, we avoid overfitting to specific deepfake generators altogether. Such models can be trained in a multi-task manner, e.g., to jointly reconstruct disturbed real faces and perform deepfake detection [87]. Note that this strategy bears similarities to the face reconstruction used in the VAE of network B in our proposed GenConViT, and hence may be a particularly viable direction for future research.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Held-Out** & **Test Set** & **Acc. (\%)** & **Real Acc. (\%)** & **Fake Acc. (\%)** \\ \hline \multirow{4}{*}{Celeb-DF (v2)} & TM & 88.30 & 87.38 & 89.19 \\  & TIMIT & 99.96 & 99.91 & 100.00 \\  & DFDC & 89.11 & 89.11 & 89.11 \\  & FF++ & 99.56 & 99.71 & 95.24 \\  & **Celeb-DF (v2)** & **55.67** & 99.78 & **11.56** \\ \hline \multirow{4}{*}{DFDC} & TM & 88.30 & 87.38 & 89.19 \\  & TIMIT & 99.42 & 98.72 & 100.00 \\  & FF++ & 99.53 & 99.64 & 96.53 \\  & Celeb-DF (v2) & 97.83 & 97.11 & 98.56 \\  & **DFDC** & **62.08** & 95.31 & **28.84** \\ \hline \multirow{4}{*}{FF++} & TM & 88.88 & 89.76 & 88.04 \\  & TIMIT & 99.61 & 99.15 & 100.00 \\  & Celeb-DF (v2) & 96.92 & 95.67 & 98.18 \\  & DFDC & 88.53 & 91.29 & 85.78 \\  & **FF++** & **64.86** & 86.78 & **44.20** \\ \hline \multirow{4}{*}{TIMIT} & TM & 88.38 & 88.81 & 87.97 \\  & Celeb-DF (v2) & 96.77 & 94.49 & 99.04 \\  & DFDC & 88.66 & 99.36 & 87.96 \\  & FF++ & 99.51 & 99.68 & 94.59 \\  & **TIMIT** & 93.44 & 95.98 & 91.36 \\ \hline \multirow{4}{*}{TM} & TIMIT & 99.88 & 99.74 & 100.00 \\  & DFDC & 88.76 & 91.93 & 85.58 \\ \cline{1-1}  & FF++ & 99.67 & 99.84 & 94.72 \\ \cline{1-1}  & Celeb-DF (v2) & 97.73 & 98.53 & 96.93 \\ \cline{1-1}  & **TM** & **76.80** & 97.78 & **56.66** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ablation results across all training scenarios (40k samples).