Summary of Certified Adversarial Robustness Via Partition-based Randomized Smoothing, by Hossein Goli et al.
Certified Adversarial Robustness via Partition-based Randomized Smoothing
by Hossein Goli, Farzan Farnia
First submitted to arxiv on: 20 Sep 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 proposes a new approach, Pixel Partitioning-based Randomized Smoothing (PPRS), to improve the robustness of deep neural network classifiers against adversarial perturbations. Specifically, PPRS boosts the confidence score and certified prediction radius by partitioning high-dimensional images into smaller regions and applying randomized smoothing to each region separately. The authors demonstrate that this approach improves the visibility of images under additive Gaussian noise and significantly enhances the certified accuracy and stability of the prediction model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, researchers have been trying to make sure artificial intelligence models are reliable and can’t be easily fooled by fake information. They found a way to improve how well these models work when there’s some random noise added to the pictures they’re looking at. This new method makes it easier for AI models to recognize what’s real and what’s not, even if someone tries to trick them. |
Keywords
» Artificial intelligence » Neural network
