Summary of Machine Unlearning Fails to Remove Data Poisoning Attacks, by Martin Pawelczyk et al.
Machine Unlearning Fails to Remove Data Poisoning Attacks
by Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu, Gautam Kamath, Ayush Sekhari, Seth Neel
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computers and Society (cs.CY)
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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 The paper investigates the effectiveness of several approximate machine unlearning methods in deep learning settings. These methods were initially designed to comply with data deletion requests, but they have also been proposed as a solution to remove training effects on poisoned data. The authors experimentally demonstrate that existing unlearning methods fail to remove the impact of poisoning attacks, including indiscriminate, targeted, and Gaussian attacks, across various models (image classifiers and large language models) and evaluation settings. To better understand unlearning efficacy, new metrics are introduced based on data poisoning. The results suggest that a broader perspective is needed to avoid overconfidence in machine unlearning procedures for deep learning without guarantees. While some unlearning methods show promise in efficiently removing poisoned datapoints, they currently offer limited benefits compared to retraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well certain methods work for “unlearning” or forgetting things we learned from bad data. We often need to forget what we learned from bad data so that it doesn’t affect our future decisions. The authors tested these methods and found out that they don’t really work when the data is poisoned, which means someone intentionally added incorrect information to make us make mistakes. They also came up with new ways to measure how well these methods work. The results show that we need to be careful not to get overconfident in our methods because they’re not perfect yet. |
Keywords
* Artificial intelligence * Deep learning
