A Deepfake Detection Model Based on the Integration of ResNet50 and LSTM

Video processing has consistently been a focal point of interest in both the media industry and academia, particularly against the backdrop of continuous advancements in digital signal processing technology. These videos are sometimes misused to disseminate false information or damage reputations. This paper aims to develop a video authenticity recognition system using advanced deep learning models. In terms of technical implementation, the ResNet50 model is employed for image feature extraction to detect subtle inconsistencies and artifacts, such as unnatural facial movements and irregular lighting variations in videos. Additionally, LSTM is used to capture the temporal sequence features of the video. Experimental results indicate that the model achieves an accuracy rate of 81.25% in detecting deepfake videos. Additionally, the model achieved a recall rate of 1, which holds significant practical value. This achievement provides strong support for the accurate detection and labeling of videos, playing a crucial role in preserving information authenticity and ensuring media security.