## 3 Results  
### Dataset Introduction  
The dataset used in this experiment comes from the largest publicly available deepfake dataset, the DFDC (DeepFake Detection Challenge) dataset [9], which was released by the Facebook AI team in 2020. This dataset contains 128,154 videos, of which 104,500 are deepfake videos. Each video is approximately 10 s long with frame rates and resolutions ranging from low to high. The individuals in the dataset are actors from diverse backgrounds, genders, ages, and ethnicities, ensuring the dataset's diversity. In contrast to earlier deepfake datasets, all individuals in the DFDC dataset have given their consent for their images to be included and have authorized alterations to their images during the dataset creation process. The purpose of this dataset is to train and test deepfake detection models, conduct related research and analysis, promote the development of deepfake detection technology, and organize corresponding deepfake detection competitions.  
Based on the DFDC dataset, this paper selected a portion of real videos and fake videos. To ensure a sufficient number of image samples for analysis, it is a common practice to extract multiple frames per second from video data. For instance, frames can be sampled at regular intervals such as every second, or a subset of frames, for example, 10 frames per second, can be systematically selected. This approach enhances the richness and diversity of the dataset, which is particularly crucial for deepfake detection tasks. A large and varied sample size is essential to effectively capture the diverse forgery patterns present in deepfake images. The videos were processed frame by frame to generate a collection of real and fake face images, amounting to nearly 22,000 images in total with approximately 11,000 real faces and 11,000 fabricated faces. The selected images include low-quality images caused by factors such as high light intensity leading to overexposure, low light intensity leading to underexposure, low resolution, and different shooting angles. Deepfake images frequently exhibit abnormal lighting conditions, which can result in overexposure or underexposure due to excessively strong or weak illumination. Additionally, low-resolution deepfake images often lose significant details, leading to blurry facial features. Furthermore, deepfake techniques may introduce unnatural distortions or misalignments in key facial attributes, such as the eyes, mouth, and nose. In some cases, discrepancies in facial angles between deepfake images and real videos may arise due to imperfections in image synthesis. These subtle artifacts serve as critical indicators for deepfake detection models. The labeling was completed using the Labelimg annotation software, classifying the images into two labels: \"true\" (real) and \"fake\" (fake). The total of approximately 22,000 face images was split into training, testing, and validation sets: around 18,000 images were designated for the training set, 2000 images were assigned to the testing set, and an additional 2000 images were designated for the validation set. These divisions not only ensure the adequacy of the training data but also guarantee that the model can undergo effective validation and testing.