Research on Real-Time Deepfake Image Detection System Based on a Self-Supervised Learning Framework

The rapid evolution of deepfake technology raises significant security and ethical concerns, as its potential misuse could lead to the spread of disinformation. In this study, we propose an advanced real-time deepfake image detection system based on a self-supervised learning framework. The model is based on the most advanced visual transformers (ViTs) and integrates contrastive representation learning in order to effectively distinguish between manipulated content and real images. Furthermore, we have incorporated a temporal consistency module that leverages subtle temporal artefacts to enhance the precision of detection, particularly in video frame sequences. The architectural design is devised to capture spatial and temporal anomalies, thereby ensuring robust detection performance in real-time scenarios. The experimental outcomes on benchmark datasets of Celeb-DF demonstrate that the proposed model exhibits a notable superiority to the existing deepfake detection methods in terms of accuracy and efficiency.