# DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer
## 5 Results & Discussion
### Intra-Dataset Evaluation  
The main goal of this section is to investigate the learning capability of the model and see how well it performs against datasets with different visual qualities and challenging real-world deepfake datasets. Therefore, the model is trained and tested on a range of deepfake detection datasets, spanning different levels of visual quality scores, namely Celeb-DF (V2), WildDeepfake, and FaceForensics++ [49, 55, 56]. The findings of this experiment demonstrated that DFDT performs significantly well on every challenging dataset on all measured scores. Quantitative frame-level detection results are summarized in Table 2. Particularly, in the frame-level setting the DFDT model has achieved 99.41%, 99.31%, and 81.35% on an accuracy score corresponding to FaceForensics++ (raw) [49], Celeb-DF (V2) [55], and WildDeepfake [56], respectively. A similar trend is apparent in video-level analysis, providing additional strong evidence regarding the outstanding performance of the proposed transformer-based deepfake detection approach in intradataset settings. Furthermore, a quantitative comparison of the existing deepfake detection methods with DFDT on every dataset is presented in Table 3. Note that the same evaluation metrics as the literature are utilized for each dataset. It can be observed from Figure 5 that the presented deepfake detection approach in this work outperforms existing methods on all three benchmarks. This figure also reveals another critical point: although most deepfake detection approaches perform well on relatively more straightforward datasets, i.e., FaceForensics++, their performance is still far from perfect on more challenging and real-world datasets, i.e., WildDeepfake.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset \(\downarrow\)** & **Metrics \(\rightarrow\)** & **ACC (\%)** & **AUC (\%)** \\ \hline \multirow{4}{*}{FaceForensics++} & (LQ) & 93.67 & 94.48 \\  & (HQ) & 98.18 & 99.26 \\  & (Raw) & 99.41 & 99.94 \\ \hline \multirow{2}{*}{Celeb-DF (V2)} & & 99.31 & 99.26 \\ \hline \multicolumn{2}{c}{WildDeepfake} & & 81.35 & 80.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative detection results on different deepfake forensics benchmarks.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods \(\downarrow\)** & **FaceForensics++** & **Methods \(\downarrow\)** & **Celeb-DF (V2)** & **Methods \(\downarrow\)** & **WideDeepfake (AR \%)** \\ \hline Two-stream [63] & 70.1 & Two-stream [63] & 53.8 & AlexNet [64] & 60.37 \\ Meso4 [35] & 84.7 & Meso4 [35] & 54.8 & VGG16 [65] & 60.92 \\ HeadPose [10] & 47.3 & HeadPose [10] & 54.6 & ResNetV2-50 [66] & 63.99 \\ FWA [12] & 80.1 & FWA [12] & 56.9 & ResNetV2-101 [66] & 58.73 \\ VA-MLP [34] & 66.4 & VA-MLP [34] & 55.0 & ResNetV2-152 [66] & 59.33 \\ Xception-raw [49] & 99.7 & Xception-c40 [49] & 65.5 & Inception-v2 [67] & 62.12 \\ Multi-task [68] & 76.3 & Multi-task [68] & 54.3 & MesoNet-I [35] & 60.51 \\ Capsule [69] & 96.6 & Capsule [69] & 57.5 & MesoNet-4 [35] & 64.47 \\ DSP-FWA [12] & 93 & DSP-FWA [12] & 64.6 & MesoNet-inception [35] & 66.03 \\ TBRN [70] & 93.2 & TBRN [70] & 73.4 & XceptionNet [47] & 69.25 \\ SPSL [71] & 96.94 & Face X-ray [72] & 80.5 & ADDNet-2D [56] & 76.25 \\ F3-Net [73] & 97.97 & SPSL [71] & 76.8 & ADDNet-3D [56] & 65.5 \\ Video SE [46] & 99.64 & F3-Net [73] & 65.1 & ADD-Xception [18] & 79.23 \\ RNN [74] & 83.10 & PPA [75] & 83.1 & \\  & & DefakeHop [6] & 90.5 & \\  & & FakeCatcher [15] & 91.5 & \\  & & ATS-DE [7] & 97.8 & \\  & & ADD-ResNet [18] & 98.3 & \\ \hline DFDT (Ours) & 99.7 & DFDT (Ours) & 99.2 & DFDT (Ours) & 81.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A quantitative comparison of DFDT’s performance on every dataset with existing deepfake detection approaches in frame-level analysis. Reported results are obtained from associated articles. The same evaluation metric as the literature is used for each dataset to provide a fair comparison and better insight into the model’s performance.