Survey details model inversion attacks on deep neural networks

A new survey paper details model inversion attacks (MIAs) that can exploit deep neural networks to reconstruct training data or infer sensitive information. These attacks are effective across various data types, including images, text, and graphs, raising significant privacy concerns for deployed models. The survey synthesizes existing research on MIAs and their countermeasures, comparing different approaches based on attacker knowledge, target reconstruction, and privacy-utility trade-offs. AI

IMPACT Highlights potential privacy risks in deployed AI models and surveys defenses, informing developers and researchers on mitigation strategies.

RANK_REASON This is a survey paper on a specific research topic within AI safety and privacy. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Deep Neural Networks
• Hugging Face
• Zhanke Zhou

• paper
• safety

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →