Summary of Relationship Between Uncertainty in Dnns and Adversarial Attacks, by Mabel Ogonna et al.
Relationship between Uncertainty in DNNs and Adversarial Attacks
by Mabel Ogonna, Abigail Adeniran, Adewale Adeyemo
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 Medium Difficulty Summary: Deep learning networks have revolutionized various fields by achieving state-of-the-art results and outperforming human accuracy in tasks such as natural language processing, pattern recognition, prediction, and control optimization. However, these networks are accompanied by uncertainties about their predictions, which can be outside the confidence level due to model or data constraints. Moreover, adversarial attacks aim to perturb input to DNNs, leading to incorrect predictions or increased uncertainty. This paper reviews the relationship between DNN uncertainty and adversarial attacks, highlighting how these attacks might elevate DNN uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Deep learning networks are super smart computers that can do many things better than humans. However, they’re not perfect and sometimes make mistakes. There’s even a type of attack called an “adversarial attack” that tries to trick the network into making wrong predictions or being unsure. This paper looks at how these attacks affect the networks’ uncertainty. |
Keywords
» Artificial intelligence » Deep learning » Natural language processing » Optimization » Pattern recognition
