Secure Avatars via Environmental Fingerprints for Virtual Health Monitoring Services

The emergence of the Metaverse as a virtual extension of our social and professional lives has amplified concerns about identity, security, and authenticity. Advanced Deepfake technologies enable malicious actors to create highly realistic but fraudulent avatars, posing significant risks such as identity theft, misinformation, and erosion of trust within virtual communities. In sensitive applications such as virtual healthcare, these risks can have severe consequences. This paper proposes to Secure Avatars via Environmental Fingerprints (SAVE), a novel method for detecting Deepfake avatars leveraging unique environmental information in the physical world, including Electric Network Frequency (ENF) signals and device-specific sensor readings, to establish an authentication framework that is challenging for impostors to replicate. To evaluate the effectiveness of our SAVE scheme, we conducted a case study within a virtual healthcare application: a Microverse-based nursing home designed to monitor the safety of seniors living alone. Our experimental results demonstrate that SAVE achieves a high detection rate with minimal false positives. Our findings highlight the potential of physical environmental fingerprints as a robust layer of security in virtual worlds, especially in critical applications like virtual healthcare.