# DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer
## 4 Evaluation Settings
### Datasets  
Early deepfake forensics benchmarks have significantly enhanced the community's awareness of deepfake threats and encouraged the development of different detection tools. Nonetheless, they suffer from the following drawbacks (1) limited scenes in original videos, (2) low-quality synthesized faces, (3) visible splicing boundaries, (4) color mismatch, (5) visible parts of the original face, and (6) inconsistent synthesized face orientations [55; 56]. On the other hand, as adversarial entities are constantly devising new techniques to create more decisive deepfakes, forensics tools must be comprehensive and applicable to challenging real-world applications. Therefore, every dataset selected for evaluation should hold specific characteristics, including diversified real-world scenes, minimal visual artifacts, super-realism and stealth, and covering a wide range of manipulation techniques. To satisfy this requirement, DFDT's performance is empirically examined against a wide range of high-quality yet challenging benchmarks, including FaceForensics++ [49], Celeb-DF (V2) [55] and WildDeepfake [56]. While FaceForensics++ consists in four different types of facial forgery types, Celeb-DF (V2) and WildDeepfake are the most challenging deepfake datasets in real-world scenarios. For each dataset, 80% of the video samples are held out for training purposes, whereas the the rest are equally divided into test and validation sets. Furthermore, the official test set of four other well-known benchmarks, i.e., DeeperForensics [57], Facebook's DeepFake Detection Challenge (DFDC) dataset [58], and FaceShifter [33] are utilized to evaluate the cross-dataset generalization capability of the DFDT. The purpose of this measurement is to demonstrate how well the model would perform on unseen deepfake samples. A brief description of the utilized datasets are presented as follows and associated statistical specifics are listed in Table 1.  
**FaceForensics++**. FaceForensics++ is one of the well-known deepfake detection datasets consisting of four different types of manipulation techniques, including Deepfakes [27], FaceSwap [59], Face2 Face [3], and NeuralTextures [60]. It has 1000 real videos from YouTube and corresponding deepfake videos generated using the aforementioned techniques. **Celeb-DF (V2)**. The Celeb-DF (V2) is composed of large-scale deepfake videos generated using an improved synthesis process that swaps faces of individuals in target and source videos. The Celeb-DF (V2) offers high visual quality scores and consists of 5639 deepfakes corresponding to over 2 million frames. **WildDeepfake**. Unlike FaceForensics++ and Celeb-DF (V2), the WildDeepfake dataset comprises real-world videos for both original and fake videos gathered from the Internet. They are not generated using AI-enabled methods, making them more challenging and \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Dataset**} & \multicolumn{6}{c}{**Statistics**} & \multicolumn{1}{c}{**Source**} \\ \cline{3-8} \multicolumn{2}{c}{} & **Videos** & **Frames** & **Train** & **Test** & **Val.** & \\ \hline \multirow{2}{*}{FaceForensics++ [49]} & Real & 1000 & 509.9 K & 800 & 100 & 100 & YouTube \\  & Deepfake & 4000 & 1830.1 K & 3200 & 400 & 400 & DF \\ \hline \multirow{2}{*}{Celeb-DF (V2) [55]} & Real & 590 (+300) \({}^{1}\) & 225.4 K & 632 & 62 & 196 & YouTube \\  & Deepfake & 5639 & 2116.8 K & 4736 & 536 & 340 & DF \\ \hline \multirow{2}{*}{WildDeepfake [56]} & Real & 3805 & 680 K & 3044 & 380 & 381 & Internet \\  & Deepfake & 3509 & 500 K & 2807 & 350 & 351 & Internet \\ \hline \hline \end{tabular}  
* Celeb-real plus 300 additional videos downloaded from YouTube [55].  
\end{table} 
Table 1: Statistical specifics of the three benchmarks utilized in this study. Holding diversified real-world scenes, minimal visual artifacts, super-realistic & stealthy [55; 56], and covering a wide range of manipulation techniques [49] are key characteristics of the selected benchmarks. closer to real-world scenarios.