Abstract

The security and integrity of remote healthcare systems are raised as an urgent issue of fighting deepfake technologies, voice cloning, and synthetic data. Since telemedicine platforms are increasingly relying on audiovisual communication and electronic health records (EHR), they actively become appealing victim targets in high-level impersonation attacks. An exhaustive architecture to curb threats postulated through deepfakes, through a combination of multimodal biometric (face, voice, gesture) authentication, real-time deepfake detection, and provenance tracking using blockchain is proposed in this research. We can show that the proposed system can achieve higher than 95 per cent detection accuracy and can eliminate session compromise within two seconds using simulated attack scenarios on well publicised data sets including DFDC, VoxCeleb and MIMIC-III. Our results verify that multi-layered defenses have the potential of securing clinical integrity and patient privacy without much impairment of user experience. The paper establishes the working principle of scalable, resilient, adaptive, and secure telehealth ecosystems against changing threats in synthetic media settings.

References

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