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Spoofing detection system for e-health digital twin using EfficientNet Convolution Neural Network

  • 1202: Multimedia Tools for Digital Twin
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Abstract

Digital Twin is the mirror image of any living or non-living objects. Digital Twin and Cyber-physical system (CPS) provides a new era for industries especially in the healthcare sector that keeps track of health data of individuals to provide on-demand, fast and efficient services to the users. In the suggested system, various health parameters of the patients are collected through different health instruments, wearable devices that communicate data to the primary database; used for analysis purposes for better diagnosis and training for automated systems. The primary database in a physical object and parallelly maintain virtual object/digital twin of the same in order of analyzing, summarize and mine data for diagnosis, monitoring the patient in real-time. The e-health cloud data need to be protected from unauthorized access by biometric authentication using iris biometric trait. The proposed paper suggested two phases EfficientNet Convolution Neural Network-based framework for identifying the real or spoofed user sample. The proposed system is trained using EfficientNet Convolution Neural Network on different datasets of spoofed and actual iris biometric samples to discriminate the original and spoofed one.

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Authors and Affiliations

  1. GLA University, Uttar Pradesh, Mathura, India

    Hitendra Garg, Shashi Shekhar & Rohit Agarwal

  2. Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India, Baddi

    Bhisham Sharma

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  1. Hitendra Garg
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  2. Bhisham Sharma
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  4. Rohit Agarwal
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Correspondence to Bhisham Sharma.

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Garg, H., Sharma, B., Shekhar, S. et al. Spoofing detection system for e-health digital twin using EfficientNet Convolution Neural Network. Multimed Tools Appl 81, 26873–26888 (2022). https://doi.org/10.1007/s11042-021-11578-5

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