Unseen Deepfakes

Unseen deepfakes, highly realistic synthetic media generated by unknown or novel methods, pose a significant challenge to existing detection techniques. Current research focuses on developing more generalizable detection models, employing architectures like Vision Transformers and diffusion models, and exploring multimodal approaches that leverage both audio and visual cues to identify inconsistencies. This research is crucial for mitigating the harmful effects of deepfakes, including misinformation, fraud, and identity theft, by improving the robustness and accuracy of detection methods across diverse deepfake generation techniques. The development of robust, artifact-agnostic detectors is a key area of focus, moving beyond reliance on identifying telltale signs of manipulation.