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Integrity Authentication Based on Blockchain and Perceptual Hash for Remote-Sensing Imagery

Remote Sensing, ISSN: 2072-4292, Vol: 15, Issue: 19
2023
  • 5
    Citations
  • 0
    Usage
  • 10
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    5
  • Captures
    10
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • News
        1

Most Recent News

Data on Remote Sensing Described by a Researcher at Ministry of Education (Integrity Authentication Based on Blockchain and Perceptual Hash for Remote-Sensing Imagery)

2023 OCT 20 (NewsRx) -- By a News Reporter-Staff News Editor at Math Daily News -- Investigators discuss new findings in remote sensing. According to

Article Description

The integrity of remote-sensing image data is susceptible to corruption during storage and transmission. Perceptual hashing is a non-destructive data integrity-protection technique suitable for high-accuracy requirements of remote-sensing image data. However, the existing remote-sensing image perceptual hash-authentication algorithms face security issues in storing and transmitting the original perceptual hash value. This paper proposes a remote-sensing image integrity authentication method based on blockchain and perceptual hash to address this problem. The proposed method comprises three parts: perceptual hash value generation, secure blockchain storage and transmission, and remote-sensing image integrity authentication. An NSCT-based perceptual hashing algorithm that considers the multi-band characteristics of remote-sensing images is proposed. A Perceptual Hash Secure Storage and Transmission Framework (PH-SSTF) is designed by combining Hyperledger Fabric and InterPlanetary File System (IPFS). The experimental results show that the method can effectively verify remote-sensing image integrity and tamper with the location. The perceptual hashing algorithm exhibits strong robustness and sensitivity. Meanwhile, the comparison results of data-tampering identification for multiple landscape types show that the algorithm has stronger stability and broader applicability compared with existing perceptual hash algorithms. Additionally, the proposed method provides secure storage, transmission, and privacy protection for the perceptual hash value.

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