Defending Against Model Stealing Attacks with Adaptive Misinformation
This addresses the problem of protecting proprietary models from cloning by adversaries, offering a more efficient defense with minimal computational overhead, though it is incremental as it builds on existing defense concepts.
The paper tackles model stealing attacks on deep neural networks by proposing Adaptive Misinformation, a defense that selectively sends incorrect predictions for out-of-distribution queries, reducing clone model accuracy by up to 40% while maintaining benign user accuracy with less than 0.5% impact.
Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which allows a data-limited adversary with no knowledge of the training dataset to clone the functionality of a target model, just by using black-box query access. Such attacks are typically carried out by querying the target model using inputs that are synthetically generated or sampled from a surrogate dataset to construct a labeled dataset. The adversary can use this labeled dataset to train a clone model, which achieves a classification accuracy comparable to that of the target model. We propose "Adaptive Misinformation" to defend against such model stealing attacks. We identify that all existing model stealing attacks invariably query the target model with Out-Of-Distribution (OOD) inputs. By selectively sending incorrect predictions for OOD queries, our defense substantially degrades the accuracy of the attacker's clone model (by up to 40%), while minimally impacting the accuracy (<0.5%) for benign users. Compared to existing defenses, our defense has a significantly better security vs accuracy trade-off and incurs minimal computational overhead.