Securing CNN Model and Biometric Template using Blockchain

TL;DR

This paper proposes a blockchain-based architecture to enhance the security and fault tolerance of CNN models and biometric templates, enabling tamper detection and secure distributed biometric recognition.

Contribution

It introduces a novel blockchain-enabled framework for securing deep learning models and biometric data in distributed environments, with experimental validation across multiple biometric modalities.

Findings

Enhanced security against tampering in biometric systems

Fault tolerant access in distributed biometric recognition

Effective detection of alterations in system components

Abstract

Blockchain has emerged as a leading technology that ensures security in a distributed framework. Recently, it has been shown that blockchain can be used to convert traditional blocks of any deep learning models into secure systems. In this research, we model a trained biometric recognition system in an architecture which leverages the blockchain technology to provide fault tolerant access in a distributed environment. The advantage of the proposed approach is that tampering in one particular component alerts the whole system and helps in easy identification of `any' possible alteration. Experimentally, with different biometric modalities, we have shown that the proposed approach provides security to both deep learning model and the biometric template.