  The rise of Machine Learning as a Service (MLaaS) has led to the widespread
deployment of machine learning models trained on diverse datasets. These models
are employed for predictive services through APIs, raising concerns about the
security and confidentiality of the models due to emerging vulnerabilities in
prediction APIs. Of particular concern are model cloning attacks, where
individuals with limited data and no knowledge of the training dataset manage
to replicate a victim model's functionality through black-box query access.
This commonly entails generating adversarial queries to query the victim model,
thereby creating a labeled dataset.
  This paper proposes "MisGUIDE", a two-step defense framework for Deep
Learning models that disrupts the adversarial sample generation process by
providing a probabilistic response when the query is deemed OOD. The first step
employs a Vision Transformer-based framework to identify OOD queries, while the
second step perturbs the response for such queries, introducing a probabilistic
loss function to MisGUIDE the attackers. The aim of the proposed defense method
is to reduce the accuracy of the cloned model while maintaining accuracy on
authentic queries. Extensive experiments conducted on two benchmark datasets
demonstrate that the proposed framework significantly enhances the resistance
against state-of-the-art data-free model extraction in black-box settings.
