Name
Abstract | ||
|---|---|---|
In this article we provide a general framework using Ethereum smart contracts to track back the provenance and evolution of deep learning content (DLC) to its original source even if the DLC was edited (e.g. DL models were retrained or/and datasets were updated) by anonymous authors. The main principle behind the solution is that if the DLC can be credibly traced to a trusted or reputable source, ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/EUROCON52738.2021.9535621 | IEEE EUROCON 2021 - 19th International Conference on Smart Technologies |
Keywords | DocType | ISBN |
Deep learning,Conferences,Smart contracts,Medical services,Back,History,Reliability | Conference | 978-1-6654-3299-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Volodymyr Valko | 1 | 0 | 0.34 |
| Sergii Stirenko | 2 | 53 | 14.13 |
| Ihor Babarykin | 3 | 0 | 0.34 |
| Yuri G. Gordienko | 4 | 50 | 8.93 |