AI RESEARCH
Model Privacy: A Unified Framework for Understanding Model Stealing Attacks and Defenses
arXiv CS.LG
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ArXi:2502.15567v3 Announce Type: replace The use of machine learning (ML) has become increasingly prevalent in various domains, highlighting the importance of understanding and ensuring its safety. One pressing concern is the vulnerability of ML applications to model stealing attacks. These attacks involve adversaries attempting to recover a learned model through limited query-response interactions, such as those found in cloud-based services or on-chip artificial intelligence interfaces.