Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients (1711.09404v1)

Abstract: Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their predictions are uninterpretable, and the predictions themselves can often be fooled by small adversarial perturbations. These problems pose major obstacles for the adoption of neural networks in domains that require security or transparency. In this work, we evaluate the effectiveness of defenses that differentiably penalize the degree to which small changes in inputs can alter model predictions. Across multiple attacks, architectures, defenses, and datasets, we find that neural networks trained with this input gradient regularization exhibit robustness to transferred adversarial examples generated to fool all of the other models. We also find that adversarial examples generated to fool gradient-regularized models fool all other models equally well, and actually lead to more "legitimate," interpretable misclassifications as rated by people (which we confirm in a human subject experiment). Finally, we demonstrate that regularizing input gradients makes them more naturally interpretable as rationales for model predictions. We conclude by discussing this relationship between interpretability and robustness in deep neural networks.

• The paper introduces input gradient regularization to boost both adversarial robustness and interpretability in deep neural networks.
• Gradient-regularized models retain higher accuracy on adversarial examples compared to baseline defenses like defensive distillation and adversarial training.
• The approach produces well-behaved gradient distributions that align model predictions with human reasoning, enhancing trust and transparency.

Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients

The paper, authored by Andrew Slavin Ross and Finale Doshi-Velez, examines adversarial robustness and interpretability in deep neural networks (DNNs) through the lens of input gradient regularization. The work systematically addresses two prominent challenges in DNNs: vulnerability to adversarial attacks and lack of interpretability in model predictions. It proposes a novel defense mechanism that regularizes input gradients to mitigate these issues.

Key Contributions

The primary contribution of the paper lies in the introduction of input gradient regularization as a dual approach to enhance both robustness and interpretability in DNNs. By minimizing the influence of small input perturbations on the output, the authors posit that models not only become more resistant to adversarial attacks but also produce more interpretable misclassifications, as validated through human subject experiments.

The authors evaluate their approach across multiple datasets, attacks, and model architectures. They demonstrate significant improvements in robustness to transferred adversarial examples. Specifically, models trained with gradient regularization maintained higher accuracy against adversarial examples compared to other baseline defenses such as defensive distillation and adversarial training.

Experimental Findings

The experiments employed a series of adversarial attacks, including the Fast Gradient Sign Method (FGSM), Targeted Gradient Sign Method (TGSM), and Jacobian-based Saliency Map Approach (JSMA). Key observations include:

• Robustness: Gradient-regularized models demonstrated superior robustness, maintaining accuracy on adversarial examples designed to fool alternative models.
• Interpretability: The adversarial examples generated from gradient-regularized models appeared more legitimate and interpretable to human subjects, suggesting that these models make predictions that align more closely with human reasoning.

The paper further explores the statistical properties of input gradients across different defense mechanisms. Gradient regularization leads to well-behaved gradient distributions, contrasting sharply with exploded gradients observed in distillation methods.

Theoretical and Practical Implications

Theoretically, this research suggests a potential inverse relationship between interpretability and adversarial vulnerability in DNNs. The regularization of input gradients appears to be a promising approach to balance this trade-off, presenting an avenue for future exploration in developing robust and interpretable machine learning models.

Practically, the proposed method could enhance the deployment of DNNs in security-sensitive domains, such as autonomous driving and healthcare, where interpretability of predictions is crucial. The implications extend to improving trust and transparency in AI systems, potentially leading to broader acceptance and integration.

Future Directions

The paper opens the door for various future investigations. One area could focus on optimizing hyperparameters specific to gradient regularization to balance robustness and accuracy. Additionally, exploring the scalability of input gradient regularization to larger and more complex network architectures represents another promising direction.

Further research might also look into combining gradient regularization with other techniques to build comprehensive defense frameworks against adversarial attacks. The potential cross-application of these concepts in other machine learning paradigms and multi-modal tasks remains an intriguing prospect.

Overall, this research provides significant insights into leveraging input gradient regularization to enhance both the robustness and interpretability of deep neural networks, offering valuable contributions to the ongoing discourse in adversarial machine learning and explainable AI.