Deepfake-Resistant Telehealth: Multi-Factor Voice-Face-Contextive Verification Under Real-Time Constraints

Telehealth's high growth rate has raised the apprehensions that the impersonation attacks, which have been conducted due to the deepfake technologies, are dangerous to patient safety and compliance with regulations. The proposed research proposes a multi-factor verification with voice spectra analysis, facial micro-motion, and contextual metadata (IP, device, timing) with real-time limitations. The system exploits lightweight CNN and transformer encoders that are privacy-preserving by federated learning and differential privacy. Implemented using SOC workflow integration and deployed by using WebRTC middleware, the framework demonstrated an improvement in true positive rate by 295%, a false positive rate of less than 3%, and a latency of less than 250 milliseconds. Findings also identify resistance to adversarial perturbations and multi-strategy deepfakes. Artifacts of evidence, such as an AI intrusion detection prototype and the impact of teaching, support the applicability of the system. The paper concludes with policy implications about HIPAA/GDPR compliance and establishes future directions, such as IoT wearables, federated adversarial training, and zero-trust telehealth architecture.