A secured distributed detection system based on IPFS and blockchain for industrial image and video data security
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pHash and its variants operate in the frequency domain, extracting low-frequency DCT coefficients to capture global structural information while suppressing high-frequency noise.
“In contrast, pHash and its variants operate in the frequency domain, extracting low-frequency DCT coefficients to capture global structural information while suppressing high-frequency noise12.”
From DBpHash: a blockchain-based dual-band perceptual hashing framework for copyright protection of purely chromatic background images (2026) · cites this paper as [12]
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- Authors
- Kumar, R., Tripathi, R., Marchang, N., Srivastava, G., Gadekallu, T. R., & Xiong, N. N.
- Affiliations
- National Institute of Technology Raipur (IN)
- North Eastern Regional Institute of Science and Technology (IN)
- China Medical University (TW)
- Vellore Institute of Technology University (IN)
- Corresponding author
- Srivastava, Gautam
- ORCID
- Venue
- Journal of Parallel and Distributed Computing
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 152
- Pages
- 128-143
- ISSN
- 0743-7315 (ISSN-L)
- OpenAlex
- W3135191333
- Cited by
- 140
- References
- 61
- Citations by year
- 2026: 8 · 2025: 24 · 2024: 28 · 2023: 36 · 2022: 30 · 2021: 14
- FWCI
- 22.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Parallel and Distributed Computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- copyright infringement detection, image forgery detection, video tampering detection, IPFS, blockchain, perceptual hashing, multimedia security, distributed detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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