Summary of A Geometric Framework For Adversarial Vulnerability in Machine Learning, by Brian Bell
A Geometric Framework for Adversarial Vulnerability in Machine Learning
by Brian Bell
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); 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 A novel mathematical framework is proposed to understand the intriguing vulnerability observed in artificial neural networks, building upon prior work by Szegedy et al. (2013). The framework aims to support increasingly sophisticated conjectures about adversarial attacks, with a focus on the “Dimpled Manifold Hypothesis” by Shamir et al. (2021). The paper consists of two chapters: the first provides an overview of neural network architectures and their history, while the second delves into the background of adversarial vulnerability, starting from the seminal work by Szegedy et al. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is trying to understand why some computer networks can be tricked easily. Scientists want to build a strong foundation for understanding these tricks. They will develop new tools that can help with more complex problems in the future. The researchers will explore how neural networks, like those used in AI, can be fooled by small changes. This might seem simple, but it’s important for making sure AI systems are safe and reliable. |
Keywords
* Artificial intelligence * Neural network
