Summary of Standard-deviation-inspired Regularization For Improving Adversarial Robustness, by Olukorede Fakorede et al.
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness
by Olukorede Fakorede, Modeste Atsague, Jin Tian
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 standard-deviation-inspired (SDI) regularization term is designed to improve the adversarial robustness and generalization of deep neural networks (DNNs). Building on Adversarial Training (AT), which trains DNNs using generated adversarial examples, the SDI term modifies the inner maximization step in AT. By maximizing a modified standard deviation of the model’s output probabilities, the SDI term complements the outer minimization of AT and enhances robustness against stronger attacks like CW and Auto-attack, while also improving generalization. The proposed method is evaluated through experimental results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes deep neural networks (DNNs) more robust to attacks by adding a special kind of penalty to their training process. This “standard-deviation-inspired” (SDI) penalty helps DNNs do better when they’re faced with tricky examples designed to make them fail. The researchers tested this new approach and found that it makes DNNs more resistant to strong types of attacks, like CW and Auto-attack, and also improves how well they generalize to new situations. |
Keywords
» Artificial intelligence » Generalization » Regularization
