Defending Against Model Stealing Attacks with Adaptive Misinformation (1911.07100v1)

Abstract: 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.

• The paper introduces Adaptive Misinformation, a defense that misleads adversarial, out-of-distribution queries to hinder model cloning.
• It leverages the OOD nature of queries and delivers uncorrelated misinformation, maintaining high accuracy for legitimate users.
• Empirical results show a reduction in clone model accuracy by up to 40% while affecting benign performance by less than 0.5%.

An Analysis of "Defending Against Model Stealing Attacks with Adaptive Misinformation"

The paper "Defending Against Model Stealing Attacks with Adaptive Misinformation" by Kariyappa and Qureshi addresses the prevalent issue of model theft in machine learning, particularly focusing on Deep Neural Networks (DNNs). Model stealing presents a significant challenge to maintaining the confidentiality and competitive advantage of DNN-based services, as adversaries can replicate a model's functionality through black-box query access without any prior knowledge of the training data. This paper proposes an innovative defense mechanism, termed "Adaptive Misinformation" (AM), specifically designed to counteract such attacks by leveraging the inherent characteristics of adversarial queries.

Model Stealing Attacks: Mechanism and Implications

Model stealing attacks allow adversaries to clone target models by generating synthetic data or using surrogate datasets to query them. Despite constraints such as limited data availability and black-box access, adversaries can achieve clone models with high classification accuracy. The potential misuse of these cloned models poses substantial threats, including unauthorized service replication, privacy violations, and enhanced adversarial attacks. The paper highlights the necessity of robust defenses to protect the confidentiality of machine learning models and reduce these risks.

Adaptive Misinformation: A Novel Defense

The core contribution of this paper is the introduction of Adaptive Misinformation, a targeted defense strategy that exploits the Out-Of-Distribution (OOD) nature of adversarial queries. Through comprehensive analysis, the authors identify that all existing model stealing attacks rely heavily on OOD samples. This observation underpins the AM approach, which selectively delivers incorrect predictions in response to OOD inputs while maintaining the accuracy for in-distribution data.

Advantages of Adaptive Misinformation:

1. Adaptive Targeting: By focusing on OOD queries, AM minimizes the adverse effects on legitimate users, thus optimizing the trade-off between security and accuracy.
2. Uncorrelated Misinformation: Unlike previous perturbation-based defenses that maintain some correlation with the original model outputs, AM utilizes a misinformation function that generates predictions devoid of such correlations.
3. Efficiency and Scalability: The defense mechanism is computationally efficient, requiring only modest additional resources over an undefended model. This efficiency is crucial for deployment in real-time systems.

Empirical Evaluation

The empirical analysis demonstrates the efficacy of AM in degrading adversary clone accuracy by up to 40% while affecting benign user accuracy by less than 0.5%. Compared to existing defenses, AM offers a superior security-to-accuracy trade-off across various datasets, including CIFAR-10 and Flowers-17, indicating its effectiveness in practical applications.

Implications and Future Directions

This research provides a significant step towards enhancing the security of DNN models against model stealing threats. Its methodological innovation in detecting and misleading adversarial queries offers a promising direction for building resilient AI systems. The theoretical implications suggest that leveraging the distributional properties of adversarial inputs can be a powerful tool for establishing defenses in machine learning security.

Looking forward, further research could explore the integration of AM with other defense strategies to enhance its robustness against adaptive adversaries. Additionally, broader application domains beyond classification tasks could benefit from similar approaches to ensure data privacy and intellectual property protection in AI systems.

In conclusion, Kariyappa and Qureshi's exploration of Adaptive Misinformation showcases a methodologically sound and practically relevant defense, advancing our understanding and capabilities in securing machine learning models from adversarial exploitation.