Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers (2406.18451v3)

Abstract: Despite extensive research on adversarial training strategies to improve robustness, the decisions of even the most robust deep learning models can still be quite sensitive to imperceptible perturbations, creating serious risks when deploying them for high-stakes real-world applications. While detecting such cases may be critical, evaluating a model's vulnerability at a per-instance level using adversarial attacks is computationally too intensive and unsuitable for real-time deployment scenarios. The input space margin is the exact score to detect non-robust samples and is intractable for deep neural networks. This paper introduces the concept of margin consistency -- a property that links the input space margins and the logit margins in robust models -- for efficient detection of vulnerable samples. First, we establish that margin consistency is a necessary and sufficient condition to use a model's logit margin as a score for identifying non-robust samples. Next, through comprehensive empirical analysis of various robustly trained models on CIFAR10 and CIFAR100 datasets, we show that they indicate high margin consistency with a strong correlation between their input space margins and the logit margins. Then, we show that we can effectively and confidently use the logit margin to detect brittle decisions with such models. Finally, we address cases where the model is not sufficiently margin-consistent by learning a pseudo-margin from the feature representation. Our findings highlight the potential of leveraging deep representations to assess adversarial vulnerability in deployment scenarios efficiently.

• The paper introduces margin consistency as a proxy for input space margins, efficiently detecting brittle decisions in robust models.
• Empirical results on CIFAR-10 and CIFAR-100 show a strong correlation between logit and input margins, validated by metrics like AUROC and FPR@95.
• A neural mapping to pseudo-margins is proposed to enhance detection efficacy, providing a scalable method for evaluating model robustness in high-stakes applications.

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

In the paper "Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers," the authors introduce and explore the concept of margin consistency as a foundational property for identifying brittle or non-robust decisions in robustly trained deep learning models. This work is positioned within the context of enhancing the real-time applicability of AI models, particularly in scenarios demanding high levels of assurance, such as healthcare, autonomous driving, and aeronautics, where the consequences of errors can be significant.

Margin Consistency and Methodology

The authors define margin consistency as a necessary and sufficient criterion to use the logit margin as a reliable proxy for the input space margin. In robustly trained models, input space margins demonstrate the proximity of a sample to the decision boundary, providing an invaluable metric for adversarial vulnerability. However, computing these margins directly in deep networks is computationally expensive. Thus, leveraging logit margins—derived from differences in logits at the neural network's output—offers a more efficient alternative. Margin consistency, therefore, indicates a monotonic relationship between input and logit margins, enabling the detection of non-robust samples via logit margins without extensive computational overhead.

Empirical Evaluation

The paper's empirical evaluations are conducted on CIFAR-10 and CIFAR-100 datasets using various adversarially trained models from the RobustBench zoo. The paper finds a strong correlation between input margins and logit margins, suggesting a broad prevalence of margin consistency across robust models. Key performance metrics of AUROC, AUPR, and FPR@95 illustrate the efficacy of using logit margins for detecting non-robust instances. Particularly, models showing stronger margin consistency exhibit higher robustness in empirical tests compared to those with weaker consistency.

For models exhibiting weak margin consistency, the paper introduces a mechanism to map feature representations to a pseudo-margin, thereby improving the correlation and detection efficacy of brittle decisions. The learning of pseudo-margins involves a simple neural network architecture trained to optimize rank correlation between input margins and mapped pseudo-margins.

Implications and Future Directions

The implications of this research are significant in practical AI deployments. By efficiently detecting potentially brittle decisions through the property of margin consistency, the paper provides a pathway to enhanced robust accuracy evaluation without necessitating large-scale adversarial tests. In particular, this approach allows for scalable, sample-efficient robust accuracy estimation by examining only a subset of the data.

The paper further discusses potential limitations, including the constraints posed by attack-based verification strategies and the impact of neural collapse during the terminal phases of training. Nevertheless, the findings suggest new avenues of research into the provable connections between input margins and deep feature representations, encouraging further exploration into optimizing network architectures and training strategies for improved robustness.

Conclusion

This paper contributes a substantive methodology for efficiently assessing the robustness of deep learning models against adversarial attacks, emphasizing the theoretical and practical possibilities of margin consistency. The research provides a robust framework for extending the capability of AI systems where robust decision-making is crucial, situating margin consistency as a valuable tool for real-world AI system development and deployment. Future work could explore the integration of these findings into standard robustness verification workflows and extend into more complex scenarios and model architectures.