Universal Deepfake

Universal deepfake detection aims to create robust systems capable of identifying synthetically generated images and videos regardless of the underlying generation method. Current research focuses on improving the generalization capabilities of detectors, exploring techniques like frequency-domain analysis, vision-language models, and masked image modeling to learn source-agnostic features and mitigate overfitting to specific deepfake generators. This field is crucial for combating the spread of misinformation and malicious deepfakes, impacting areas such as cybersecurity, media verification, and law enforcement.