Summary of Pureebm: Universal Poison Purification Via Mid-run Dynamics Of Energy-based Models, by Omead Pooladzandi et al.
PureEBM: Universal Poison Purification via Mid-Run Dynamics of Energy-Based Models
by Omead Pooladzandi, Jeffrey Jiang, Sunay Bhat, Gregory Pottie
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackles the pressing issue of data poisoning attacks on machine learning models, where adversaries inject malicious examples during training to misclassify target distribution data. Existing state-of-the-art (SoTA) defense methods are limited by reduced generalization performance and significant training overhead, making them impractical for real-world applications. To address this challenge, the authors introduce a universal data purification method that defends naturally trained classifiers from white-, gray-, and black-box image poisons using a stochastic preprocessing step, realized by iterative Langevin sampling of an Energy-Based Model (EBM) initialized with an image. This approach purifies poison information while minimizing impact on features important to generalization. The authors demonstrate the effectiveness of their method, achieving SoTA defense on leading triggered and triggerless poisons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine someone trying to trick a machine learning model by giving it fake information during training. This can make the model misclassify things later on. Right now, there are ways to defend against this kind of attack, but they have some drawbacks. The authors of this paper have come up with a new way to purify the data and keep the model honest. They use something called an Energy-Based Model (EBM) to look for and remove the fake information from the training data. This approach is very effective at defending against different types of attacks, even the sneaky ones that are hard to detect. |
Keywords
» Artificial intelligence » Energy based model » Generalization » Machine learning
