Model Stealing

Model stealing is a form of attack where an adversary attempts to replicate or extract a machine learning model's parameters or functionality without direct access to the model itself. This is typically achieved by querying the model with various inputs and analyzing the outputs to infer its behavior. Characteristics of model stealing include the use of techniques such as black-box extraction, where the attacker does not know the internal workings of the model but can still gather sufficient information to create a surrogate model. Common use cases involve protecting intellectual property in AI systems and understanding the vulnerabilities of deployed models against potential attacks.