Summary of Towards Scalable and Robust Model Versioning, by Wenxin Ding et al.
Towards Scalable and Robust Model Versioning
by Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng
First submitted to arxiv on: 17 Jan 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 In this paper, researchers investigate the growing threat of attacks on deployed deep learning models. As these models are increasingly used across various industries, malicious actors are targeting them to gain access and manipulate their outputs. The authors highlight the risks posed by insider attacks, server breaches, and model inversion techniques, which can allow attackers to construct white-box adversarial attacks. To address this concern, the researchers aim to develop mechanisms for protecting deployed models without requiring fresh training data, thereby minimizing costly investments in time and capital. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart AI that helps with important tasks. But what if someone sneaks into your system and makes the AI do bad things? This is happening more often as AI gets used everywhere. Bad actors can break into these systems and make the AI say false things, which could be very harmful to companies or organizations. To stop this from happening, we need ways to keep our AI safe without having to start all over again with new data. |
Keywords
* Artificial intelligence * Deep learning
