Summary of Certified Causal Defense with Generalizable Robustness, by Yiran Qiao et al.
Certified Causal Defense with Generalizable Robustness
by Yiran Qiao, Yu Yin, Chen Chen, Jing Ma
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Methodology (stat.ME)
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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 GLEAN framework combines a certifiable causal factor learning component with a causally certified defense strategy to enhance certified robustness generalization across different data domains. This novel approach addresses the challenge of eliminating spurious correlations that affect robustness when dealing with distribution shifts. By leveraging a causal perspective, GLEAN disentangles causal relations and spurious correlations between input and label, effectively excluding their negative impact on defense. The framework is tested on benchmark datasets, demonstrating its superiority in certified robustness generalization across domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GLEAN is a new way to make AI models more secure by understanding how the data is related to the outcome. Most current methods are good at defending against attacks on the same type of data they were trained on, but not as good when dealing with different types of data. The GLEAN framework tries to solve this problem by looking at the underlying reasons why things happen, rather than just trying to defend against specific attacks. This approach allows it to generalize its defense capabilities to new and different situations. |
Keywords
» Artificial intelligence » Generalization
