Deepfake Detection

Deepfake detection research aims to develop robust methods for identifying manipulated media, combating the spread of misinformation and fraudulent content. Current efforts focus on improving the generalization of detection models across diverse deepfake generation techniques, employing architectures like Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), often incorporating multimodal analysis (audio-visual) and leveraging pre-trained models like CLIP. This field is crucial for maintaining digital media integrity and security, with implications for law enforcement, cybersecurity, and the broader fight against disinformation.