Authors: Hao Fang, Yixiang Qiu, Hongyao Yu, Wenbo Yu, Jiawei Kong, Baoli Chong, Bin Chen, Xuan Wang, Shu-Tao Xia

Published on: February 06, 2024

Impact Score: 8.35

Arxiv code: Arxiv:2402.04013

Summary

• What is new: This paper provides a comprehensive overview of Model Inversion (MI) attacks and defenses in the context of Deep Neural Networks (DNNs), which has not been thoroughly covered before.
• Why this is important: The issue of MI attacks enabling adversaries to reconstruct private data from pre-trained models, raising significant privacy concerns.
• What the research proposes: A detailed analysis and comparison of various recent attacks and defenses across different modalities and learning tasks, focusing on DNNs.
• Results: A holistic survey that sheds light on the strengths and weaknesses of current MI attacks and defenses, potentially guiding future research in enhancing privacy.

Technical Details

Technological frameworks used: nan

Models used: Deep Neural Networks (DNNs)

Data used: nan

Potential Impact

This research could influence companies in tech, particularly those using AI and ML in their products, by highlighting vulnerabilities and necessitating stronger defenses.

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