Summary of Taking Off the Rose-tinted Glasses: a Critical Look at Adversarial Ml Through the Lens Of Evasion Attacks, by Kevin Eykholt and Farhan Ahmed and Pratik Vaishnavi and Amir Rahmati
Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks
by Kevin Eykholt, Farhan Ahmed, Pratik Vaishnavi, Amir Rahmati
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper examines the vulnerability of machine learning models in adversarial scenarios, highlighting the lack of effective defenses despite a decade of research. While attacks on ML models have increased, the development of countermeasures has stagnated, leaving AI applications exposed to threats. The authors question whether the inability to develop solutions for existing technologies will also apply to emerging areas like generative AI and large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machine learning models are vulnerable in bad scenarios. For a long time, people have been trying to find ways to protect these models from being attacked, but it hasn’t worked. Despite this, AI is still getting better and being used more. The big question is whether the problems with making AI secure will also happen with new types of AI like those that can make things or understand language. |
Keywords
* Artificial intelligence * Machine learning
