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Method of Reaching Consensus on Probability of Food Safety Based on the Integration of Finite Credible Data on Block Chain
Open accessAbstract
The use of new technologies to empower the social co-governance of food safety is a consensus in the theoretical and practical fields of food safety governance modernization. Based on the innovative features of the block chain such as non-tampering, consensus mechanism, and a smart contract, this paper proposes a new method to facilitate the achievement of the effect of social co-governance of food safety. Firstly, it provides a profound analysis of the causes of food safety problems and the drawbacks of traditional centralized supervision, and proposes a solution with decentralized features and the design process of smart contracts to realize the chaining and integration of finite credible data in the process of food circulation; secondly, it constructs a mathematical model through finite credible data on the chain and obtains the probabilistic consensus results of food safety through rigorous derivation; Finally, the scheme, model and algorithm are effectively practiced through the experimental environment of mature block chain platform and provincial food supervision platform. The example shows that the market failure problem in food safety can be greatly alleviated by the non-tampering feature of block chain; the consensus results formed by the credible data uploading, model construction and data derivation through smart contracts can greatly alleviate the problem of involution of government administrative supervision; and the social co-governance capacity of food safety can be improved through information sharing with the active synergy of multiple subjects of co-governance.
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“They tested their suggested two-stream technique on deepfakes, FaceSwap, Face2Face, NeuralTextures, and Celeb-DF datasets, and the results outperformed previous work.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as A. Yan, Yin-He, et al. (2021)
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“The accuracy of cross-compression detection findings demonstrates that their approach is robust to compression factors.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as A. Yan, Yin-He, et al. (2021)
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“The results demonstrate that their network has fewer parameters than other networks and outperforms existing state-of-the-art deepfake detection techniques on major deepfake data sets.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as L. Yan, Yin-He, et al. (2021)
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Metadata
- Authors
- Yan, L., Yin-He, S., Qian, Y., Zhi-Yu, S., Chun-Zi, W., & Zi-Yun, L.
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 123764-123776
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3197813564
- Cited by
- 123
- References
- 8
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 11 · 2023: 73 · 2022: 28
- FWCI
- 25.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Social Science Foundation of Shaanxi Province (2020F014)
- Special Project of Shaanxi Provincial Department of Education to Serve Local Areas (19JC015)
- Project of Xi’an Science and Technology University Talents Service Enterprise (GXYD7.7)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Life Sciences › Agricultural and Biological Sciences › Food Science › Food Supply Chain Traceability
- Life Sciences › Agricultural and Biological Sciences › Food Science › Food Safety and Hygiene
- Keywords
- food safety, blockchain, smart contracts, consensus algorithms, social co-governance, food supply chain, food safety governance, market failure, information sharing
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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