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A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object

Rui Jiang1, Jiatao Li1, Weifeng Bu1

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

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|July 29, 2023
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Summary
This summary is machine-generated.

This study introduces a blockchain-based framework to enhance the trustworthiness of deep learning model evaluation. It addresses security vulnerabilities in traditional methods by ensuring secure data sharing, training, and tamper-proof evaluation results.

Keywords:
blockchaincomputer visiondeep learningmodel evaluationmoving object segmentation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Blockchain Technology

Background:

  • Traditional deep learning model evaluation faces significant trustworthiness issues, including insecure data handling, training vulnerabilities, and easily tampered results.
  • Centralized evaluation processes lack transparency and are prone to manipulation, hindering reliable model assessment.

Purpose of the Study:

  • To propose and validate a novel blockchain-based framework for secure and trustworthy deep learning model evaluation.
  • To enhance the integrity of model evaluation by addressing issues of data security, access control, and result tamper-proofing.

Main Methods:

  • A layered framework incorporating access control, secure storage (IPFS and blockchain), decentralized training, and blockchain-based evaluation.
  • Implementation of attribute-based and role-based access control using smart contracts for fine-grained security.
  • Utilizing IPFS for resource storage with blockchain for immutable record-keeping and smart contracts for automated evaluation and scoring.

Main Results:

  • The proposed framework successfully demonstrated its functionalities in deep learning-based motion object segmentation.
  • Validation confirmed the effectiveness of the storage strategy and the overall trustworthiness of the blockchain-based evaluation system.
  • Smart contracts enabled automated evaluation and secure, tamper-proof uploading of scores.

Conclusions:

  • The blockchain-based model evaluation framework significantly enhances the trustworthiness and security of deep learning model assessment.
  • The system effectively mitigates traditional vulnerabilities, offering a decentralized and transparent approach to model evaluation.
  • This framework provides a robust solution for secure resource sharing, training, and evaluation in deep learning applications.