Deepfake Detection Method

Deepfake detection research aims to develop robust methods for identifying manipulated images and videos, countering the spread of misinformation and malicious content. Current efforts focus on improving the generalizability of detection models across diverse deepfake techniques and datasets, employing architectures like Vision Transformers and convolutional neural networks enhanced with techniques such as wavelet transforms, graph convolutional networks, and attention mechanisms to extract more discriminative features. This field is crucial for safeguarding online security and combating the societal impact of fabricated media, driving advancements in both computer vision and multimedia forensics.