Summary of Adversarial Attacks For Drift Detection, by Fabian Hinder et al.
Adversarial Attacks for Drift Detection
by Fabian Hinder, Valerie Vaquet, Barbara Hammer
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the concept of concept drift in machine learning, where data distributions change over time. The authors highlight the importance of detecting drifts in system monitoring to identify malfunctions and unexpected behavior. They argue that current drift detection schemes are inadequate and demonstrate how to construct adversarial data streams that evade detection. By analyzing various detection methods and evaluating their performance empirically, the researchers aim to improve the robustness and reliability of drift detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how data changes over time in machine learning. This is important because it helps us identify when something goes wrong with a system. The authors show that current ways of detecting these changes are not good enough. They create fake data sets that can’t be detected by the usual methods and then test them to see what works best. |
Keywords
* Artificial intelligence * Machine learning
