Unlocking High-Fidelity Learning: Towards Neuron-Grained Model Extraction

Model extraction (ME) attacks replicate valuable closed-box machine learning (ML) models via malicious query interactions. Cutting-edge attacks focus on actively designing query samples to enhance model fidelity and imprudently adhere to the standard ML training approach. This causes a deviation from the true objective of learning a model over a task. In this article, we innovatively shift our focus from query selection to training process optimization, aiming to boost the similarity of the copy model with the victim model from neuron to model level. We leverage neuron matching theory to attain this objective and develop a general training booster framework, MEBooster, to fully exploit this theory. MEBooster comprises an initial bootstrapping phase that furnishes initial parameters and an optimal model architecture, followed by a post-processing phase that employs fine-tuning for enhanced neuron matching. Notably, MEBooster can seamlessly integrate with all existing model extraction attacks, enhancing their overall performance. Performance evaluation shows up to 58.10% fidelity gain in image classification. From a defender's perspective, we introduce a novel defensive strategy called Stochastic Norm Enlargement (SNE) to mitigate the risk of such attacks by enlarging the model parameters' norm property in training. Performance evaluation shows up to 58.81% extractability (i.e., fidelity) reduction.