Summary of Data-driven Subsampling in the Presence Of An Adversarial Actor, by Abu Shafin Mohammad Mahdee Jameel et al.
Data-Driven Subsampling in the Presence of an Adversarial Actor
by Abu Shafin Mohammad Mahdee Jameel, Ahmed P. Mohamed, Jinho Yi, Aly El Gamal, Akshay Malhotra
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Signal Processing (eess.SP)
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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 paper explores the use of deep learning models for automatic modulation classification (AMC) in various applications, including military and civilian uses. To overcome computational complexity and training time challenges, data-driven subsampling techniques are employed. The study finds that not only do these methods improve direct performance but also exhibit regularizing properties enhancing adversarial robustness. Medium-difficulty summary ends here. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well an automatic modulation classifier works when attacked by bad guys trying to mess it up. They use deep learning models for both classification and subsampling. The results show that using subsampling can actually make the system more resistant to attacks, and they find the most efficient way to do this. |
Keywords
* Artificial intelligence * Classification * Deep learning
