Summary of Precise Extraction Of Deep Learning Models Via Side-channel Attacks on Edge/endpoint Devices, by Younghan Lee et al.
Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices
by Younghan Lee, Sohee Jun, Yungi Cho, Woorim Han, Hyungon Moon, Yunheung Paek
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 research investigates the relationship between side-channel attacks (SCAs) on edge devices running deep learning models and model extraction attacks (MEAs). The authors analyze how MEA can exploit SCA to obtain crucial model information, such as architecture and image dimensions, ultimately enhancing its success. Notably, the study reveals that MEA can achieve high performance without prior knowledge of the victim model. The research provides a comprehensive understanding of this relationship, which can inform both offensive and defensive strategies in future MEA studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how hackers steal deep learning models by exploiting side-channel attacks on edge devices. It shows how an attacker can get important information about the model just by observing its behavior on the device. This lets them create a fake model that’s very good at mimicking the real one. The research suggests that this new attack vector can be highly effective, even without knowing anything about the original model. |
Keywords
* Artificial intelligence * Deep learning
