Adversarial Risk and the Dangers of Evaluating Against Weak Attacks (1802.05666v2)

Abstract: This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. We motivate 'adversarial risk' as an objective for achieving models robust to worst-case inputs. We then frame commonly used attacks and evaluation metrics as defining a tractable surrogate objective to the true adversarial risk. This suggests that models may optimize this surrogate rather than the true adversarial risk. We formalize this notion as 'obscurity to an adversary,' and develop tools and heuristics for identifying obscured models and designing transparent models. We demonstrate that this is a significant problem in practice by repurposing gradient-free optimization techniques into adversarial attacks, which we use to decrease the accuracy of several recently proposed defenses to near zero. Our hope is that our formulations and results will help researchers to develop more powerful defenses.

• The paper demonstrates that standard evaluation metrics based on weak attacks significantly underestimate real adversarial risk.
• It introduces a theoretical framework and experimental protocols that reveal critical vulnerabilities in machine learning models.
• The findings emphasize the need for stronger and more diverse attack strategies to improve the accuracy of model robustness assessments.

Adversarial Risk and the Dangers of Evaluating Against Weak Attacks

The paper "Adversarial Risk and the Dangers of Evaluating Against Weak Attacks" by Jonathan Uesato addresses critical issues related to the evaluation of machine learning models in adversarial settings. The focus is on how models often demonstrate vulnerabilities when exposed to adversarial attacks, and the pitfalls associated with relying on weak attacks for assessment.

Summary of Contributions

1. Evaluation Metrics for Adversarial Risk: The paper critically analyzes existing approaches to measuring adversarial risk, underscoring the inadequacy of using weak attacks as metrics. It posits that unless the attack is optimally strong, evaluating the robustness of a model can be misleading.
2. Theoretical Foundations: Uesato provides a theoretical framework that illustrates the gap in adversarial risk assessment when relying on insufficiently potent attacks. This analysis suggests that conventional methods may under-represent the model's true vulnerability.
3. Empirical Evidence: Through comprehensive experiments, the paper demonstrates that models often considered robust are vulnerable when subjected to stronger, more optimized attacks. This challenges the standard practices in adversarial testing and suggests the need for more stringent evaluation protocols.
4. Recommendations for Robust Evaluation: The author recommends adopting a more robust approach by calibrating attack strengths and leveraging a broader spectrum of attack strategies during evaluation. These recommendations aim to present a more accurate portrayal of a model’s security posture.

Implications and Future Directions

The findings in this paper have significant implications for both the design and evaluation of secure machine learning systems. Practically, they highlight the necessity for more rigorous testing environments to reliably claim model robustness. Theoretically, these insights push researchers to revisit and refine definitions and methodologies surrounding adversarial risk.

Looking forward, this work sets a foundation for further exploration into advanced attack algorithms that can better inform robustness assessments. It also opens avenues for developing models inherently resistant to a wider array of adversarial strategies. Future research should continue to focus on enhancing the fidelity of adversarial evaluations, possibly exploring automated methods to simulate stronger and more diverse attacks.

Conclusion

"Adversarial Risk and the Dangers of Evaluating Against Weak Attacks" provides a pivotal critique of existing evaluation practices in adversarial machine learning. By shedding light on the shortcomings of current methods, it encourages the research community to pursue more reliable and comprehensive strategies for assessing model robustness, thereby enhancing the security and reliability of machine learning applications in adversarial contexts.