Summary of Inverting Gradient Attacks Makes Powerful Data Poisoning, by Wassim Bouaziz et al.
Inverting Gradient Attacks Makes Powerful Data Poisoning
by Wassim Bouaziz, El-Mahdi El-Mhamdi, Nicolas Usunier
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 proposed research investigates the equivalence of gradient attacks and data poisoning in non-convex settings. It explores how these malicious techniques can harm machine learning algorithms and whether they can be used to perform availability attacks on neural networks. The study shows that data poisoning can mimic a gradient attack, allowing for the reconstruction of malicious gradients using inversion methods. This can lead to a range of attacks, including those that degrade model performance to random levels with as few as 1% poisoned points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning algorithms are being attacked by malicious techniques like gradient attacks and data poisoning. These attacks try to change how the algorithm works or what it learns. Researchers want to know if these attacks can harm neural networks too. They found that data poisoning can actually mimic a gradient attack, which means it can be used to make neural networks work poorly or not at all. This is bad news for anyone who depends on these algorithms. |
Keywords
* Artificial intelligence * Machine learning
