EchoFence: Non-Intrusive Forgery Detection in Video Conferencing via Ultrasonic Sensing

Real-time video conferencing is increasingly prevalent in everyday life, yet it faces growing threats from forgery attacks where synthetic or replayed videos are injected into live calls, potentially leading to serious privacy breaches or financial losses. Unfortunately, existing approaches remain inadequate for detecting such attacks in a reliable, efficient, and user-friendly manner. In light of this, we propose EchoFence, a non-intrusive, lightweight, and robust framework for authenticating live video streams. EchoFence actively emits imperceptible ultrasonic signals during video conferencing, which physically interact with user’s natural facial and body movements and are captured by the microphone. Motion-related features are then extracted from both the ultrasonic responses and the video frames, and a training-free cross-modal verification strategy is employed to assess their temporal coherence. Significant misalignment between the two modalities is taken as strong evidence of forgery. Additionally, each ultrasonic signal carries a random credential via frequency modulation, which is validated through template-based matching, preventing tampering attempts involving ultrasound replay or removal. Extensive experiments show that EchoFence effectively detects three representative types of video forgeries with over 94% accuracy, and remains robust under diverse conditions, making it a practical solution for trustworthy video conferencing.