<ul><li>Data-free model stealing involves replicating the functionality of a target model into a substitute model without accessing the target model's structure, parameters, or training data.</li><li>Existing methods within cooperative game frameworks often produce samples with high confidence for the prediction of the substitute model, which makes it difficult for the substitute model to replicate the behavior of the target model.</li><li>This paper presents a new data-free model stealing approach called Query Efficient Data Generation (QEDG).</li><li>The proposed method achieved better performance with fewer queries compared to the state-of-the-art methods on real MLaaS scenario and five datasets.</li></ul>

Exploring Query Efficient Data Generation towards Data-free Model Stealing in Hard Label Setting

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