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Securing CNN Model and Biometric Template using Blockchain

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TLDR
This research model a trained biometric recognition system in an architecture which leverages the blockchain technology to provide fault tolerant access in a distributed environment and shows that the proposed approach provides security to both deep learning model and the biometric template.
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.

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Citations
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Adversarial Examples—Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices

TL;DR: A number of COVID-19 diagnostic methods that rely on DL algorithms with relevant adversarial examples (AEs) are tested, showing that DL models that do not consider defensive models against adversarial perturbations remain vulnerable to adversarial attacks.
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On the Robustness of Face Recognition Algorithms Against Attacks and Bias

TL;DR: Different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working are summarized.
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Image Transformation-Based Defense Against Adversarial Perturbation on Deep Learning Models

TL;DR: This article proposes a non-deep learning approach that searches over a set of well-known image transforms such as Discrete Wavelet Transform and Discrete Sine Transform, and classifies the features with a support vector machine-based classifier, efficiently generalizes across databases as well as different unseen attacks and combinations of both.
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Measuring Decentrality in Blockchain Based Systems

TL;DR: This work identifies the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer, and quantifies decentrality in these layers using various metrics.
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A Survey on Privacy-Preserving Blockchain Systems (PPBS) and a Novel PPBS-Based Framework for Smart Agriculture

TL;DR: In this paper, the current state of privacy preservation utilising blockchain and smart contracts, as applied to a number of fields and problem domains, is outlined, and future directions of research in areas combining future technologies, privacy-preservation and blockchain.
References
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Journal ArticleDOI

Multi-PIE

TL;DR: This paper introduces the database, describes the recording procedure, and presents results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.
Proceedings Article

Multi-PIE

TL;DR: The CMU Multi-PIE database as mentioned in this paper contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions, with a limited number of subjects, a single recording session and only few expressions captured.
Journal ArticleDOI

Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability.

TL;DR: In this paper, the authors look at how blockchain technology might facilitate the transition from institution-centric to patient-centric data sharing through five mechanisms: (1) digital access rules, (2) data aggregation, (3) data liquidity, (4) patient identity and (5) data immutability.
Proceedings Article

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

TL;DR: In this article, the authors formulate the process of generating adversarial examples as an elastic-net regularized optimization problem, which can yield a distinct set of adversarial samples with small L 1 distortion.
Journal ArticleDOI

Biometric Antispoofing Methods: A Survey in Face Recognition

TL;DR: The goal of this paper is to provide a comprehensive overview on the work that has been carried out over the last decade in the emerging field of antispoofing, with special attention to the mature and largely deployed face modality.