Protecting Publicly Available Data With Machine Learning Shortcuts
Guardado en:
| Publicado en: | arXiv.org (Oct 30, 2023), p. n/a |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
Cornell University Library, arXiv.org
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2884477536 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2884477536 | ||
| 045 | 0 | |b d20231030 | |
| 100 | 1 | |a Müller, Nicolas M | |
| 245 | 1 | |a Protecting Publicly Available Data With Machine Learning Shortcuts | |
| 260 | |b Cornell University Library, arXiv.org |c Oct 30, 2023 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Machine-learning (ML) shortcuts or spurious correlations are artifacts in datasets that lead to very good training and test performance but severely limit the model's generalization capability. Such shortcuts are insidious because they go unnoticed due to good in-domain test performance. In this paper, we explore the influence of different shortcuts and show that even simple shortcuts are difficult to detect by explainable AI methods. We then exploit this fact and design an approach to defend online databases against crawlers: providers such as dating platforms, clothing manufacturers, or used car dealers have to deal with a professionalized crawling industry that grabs and resells data points on a large scale. We show that a deterrent can be created by deliberately adding ML shortcuts. Such augmented datasets are then unusable for ML use cases, which deters crawlers and the unauthorized use of data from the internet. Using real-world data from three use cases, we show that the proposed approach renders such collected data unusable, while the shortcut is at the same time difficult to notice in human perception. Thus, our proposed approach can serve as a proactive protection against illegitimate data crawling. | |
| 653 | |a Data augmentation | ||
| 653 | |a Datasets | ||
| 653 | |a Machine learning | ||
| 653 | |a Data collection | ||
| 653 | |a Data points | ||
| 700 | 1 | |a Burgert, Maximilian | |
| 700 | 1 | |a Debus, Pascal | |
| 700 | 1 | |a Williams, Jennifer | |
| 700 | 1 | |a Sperl, Philip | |
| 700 | 1 | |a Böttinger, Konstantin | |
| 773 | 0 | |t arXiv.org |g (Oct 30, 2023), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2884477536/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2310.19381 |