Real Time DeepFakes

Real-time deepfakes, generated by advanced deep learning models, pose a significant threat by enabling realistic audio and video impersonation in real-time interactions. Current research focuses on developing active defense mechanisms, such as challenge-response systems that exploit limitations in deepfake generation, and passive detection methods leveraging subtle visual cues like corneal reflections. These efforts aim to improve the accuracy and speed of deepfake detection, addressing the urgent need for robust security measures against increasingly sophisticated impersonation attacks in video conferencing and other online interactions.