Summary of Faster Repeated Evasion Attacks in Tree Ensembles, by Lorenzo Cascioli et al.
Faster Repeated Evasion Attacks in Tree Ensembles
by Lorenzo Cascioli, Laurens Devos, Ondřej Kuželka, Jesse Davis
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel method to efficiently generate adversarial examples for tree ensembles, a widely used model class in machine learning. The authors’ approach leverages the insight that these examples tend to perturb a consistent but relatively small set of features, allowing them to speed up the construction process. This is particularly significant since current methods attempt to find such examples from scratch, which can be computationally challenging. By exploiting this property, the proposed method can quickly identify the relevant features and construct adversarial examples with reduced computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to trick a machine learning model by adding tiny changes to its training data. This is called creating an “adversarial example”. It’s like trying to fool someone into thinking you’re someone else, but instead of a person, it’s a computer program. Right now, this process can be very time-consuming and requires a lot of computational power. But what if there was a way to make it faster? That’s exactly what this paper is about. The authors discovered that most “adversarial examples” for tree ensembles (a type of machine learning model) affect only a small number of features, allowing them to speed up the process of creating these trick questions. |
Keywords
* Artificial intelligence * Machine learning
