Deepfake Detection Model

Deepfake detection models aim to identify manipulated images and videos, combating the spread of misinformation and malicious content. Current research focuses on improving the generalization ability of these models across diverse deepfake techniques and datasets, employing architectures like Vision Transformers and Convolutional Neural Networks, often incorporating multimodal data (audio-visual) and advanced techniques like contrastive learning and incremental learning. This field is crucial for safeguarding digital security and public trust, driving advancements in both model robustness and fairness, as well as the development of explainable AI methods to enhance transparency and trustworthiness.