# Lightweight Deepfake Detection Based on Multi-Feature Fusion
## 4 Implementation  
### Experimental Design  
This section summarizes the experimental details and results of deepfake datasets for FaceForensic++ [24] and Celeb-DF [25]. The robustness of the fusion (LBP, HOG, and KAZE) features under various classifiers is evaluated. We applied some preprocessing on the raw data prior to experimentation with the datasets. The complete video sequence is not taken into account. Instead, as described in Section 3, some keyframes are extracted. Both fake and real videos are included in the video dataset. After being extracted, the frames were placed in a folder with the appropriate name. The NVIDIA 3090 GPU was used for feature extraction and the development of ML algorithms.  
FaceForensics++ [24] is the popular publicly available forensic dataset that includes 1000 original video sequences that have been manipulated using four distinct face manipulation techniques: deepfake, Face2 Face, FaceSwap, and NeuralTextures. The dataset consists of 977 YouTube videos with 48,431 face counts and a data size of 575 mb, all of which feature a clearly visible face, allowing automated tampering methods to produce highly accurate forgeries (an example of the dataset is depicted in Figure 2). We targeted deepfake videos and their original equivalents for our experiment. The dataset was created by extracting keyframes from several videos. Following preprocessing, there were 2946 fake images and 2930 real images in the training set. There were 198 real and 197 fake images in the validation.  
The Celeb-DF [25] dataset is divided into real and fake video/frames, where the real videos are 590 original YouTube videos with people of all ages and the fake videos are 5639 deepfakes. Keyframes from these videos were extracted to create the dataset, making sure that the images mostly featured the faces of different celebrities. The dataset consists of 100 genuine images, where 900 deepfake images make up the validation set and 1130 real images and 8022 deepfake images make up the training set. A balanced evaluation framework for deepfake detection models is provided by the test set, which consists of 340 deepfake images and 178 real images.  
There are two classes in the Celeb-DF dataset: real and fake. The percentage of fake classes is higher than that of real classes. The FaceForensics++ dataset has five classes, including 1000 videos in each class. The original video is the first class, and the other four classes are videos that have been altered/are fake. These five classes were reduced to binary classes in equal parts for the purposes of this study. To remove the dataset imbalance problem, a similar process [60] is adopted, where 800 videos were finalized from each dataset, 400 of which were chosen for each class. Following the average number of frames prepared for the experiment, the detailed dataset is displayed in Table 1.  
Figure 2: An example of fake faces from the FaceForensics++ dataset. The pristine image is in the first column, whereas the forged images produced by DeepFakes, Face2 Face, FaceSwap, and NeuralTextures are in the second through fifth columns [24].