On Adaptive Attacks to Adversarial Example Defenses (2002.08347v2)

Abstract: Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to adversarial examples. We find, however, that typical adaptive evaluations are incomplete. We demonstrate that thirteen defenses recently published at ICLR, ICML and NeurIPS---and chosen for illustrative and pedagogical purposes---can be circumvented despite attempting to perform evaluations using adaptive attacks. While prior evaluation papers focused mainly on the end result---showing that a defense was ineffective---this paper focuses on laying out the methodology and the approach necessary to perform an adaptive attack. We hope that these analyses will serve as guidance on how to properly perform adaptive attacks against defenses to adversarial examples, and thus will allow the community to make further progress in building more robust models.

• The paper demonstrates that adaptive attacks can systematically breach all thirteen evaluated adversarial defenses with tailored adjustments.
• It details a clear methodology involving defense analysis, hypothesis formulation, and iterative attack design using gradient-based techniques.
• Numerical results show drastic accuracy drops, emphasizing the need for more nuanced, robust adversarial training in future research.

On Adaptive Attacks to Adversarial Example Defenses

The paper "On Adaptive Attacks to Adversarial Example Defenses" offers a comprehensive analysis of the efficacy of various defenses against adversarial attacks. This manuscript represents a significant effort to rigorously evaluate adaptive attacks and their mechanisms, providing critical insights into the domain of adversarial machine learning.

Summary of Content

The primary focus of the paper is on adaptive attacks, which constitute a targeted approach to evaluating the robustness of defenses against adversarial examples. The authors analyze thirteen defenses recently published at high-impact conferences such as ICLR, ICML, and NeurIPS. Despite the improved evaluation techniques that involve adaptive attacks, the findings reveal that all thirteen defenses can eventually be circumvented by carefully designed attacks.

Adaptive evaluations, while becoming the standard, often fall short due to incomplete methodologies. One key message from the paper is the necessity for meticulous and context-specific tuning of adaptive attacks rather than attempting to automate them. No single strategy was sufficient to break all defenses, underlining the complexity and diversity of defense mechanisms.

Methodology and Findings

The paper dissects its evaluation process into several steps:

1. Defense Understanding: Understanding the inner workings and intentions behind each defense.
2. Initial Hypotheses: Formulating potential vulnerabilities based on readings and source code analysis.
3. Attack Design: Constructing attacks that are appropriately tuned to each defense.
4. Evaluation: Documenting the success rates and refining attacks as necessary.

Crucially, the paper emphasizes simplicity in attack design. Transparent and straightforward adaptive attacks, leveraging gradients and loss functions, are preferred to more complex strategies which might obscure potential failures.

The adaptive attacks are categorized into themes:

• Attacks focusing on the entire defense.
• Attacks identifying and targeting crucial components.
• Adapting objectives to simplify attack optimization.
• Ensuring consistent loss functions.
• Utilizing varied optimization techniques.
• Applying robust attacks, especially in adversarial training contexts.

Numerical Results

Specific defenses were evaluated rigorously, with numerical results indicating how significantly accuracy can be reduced:

• The k-Winners Take All defense demonstrated a reduction in accuracy to near 0% with appropriate black-box attacks.
• The generative classifier based on Variational Auto-Encoders saw accuracy drop below 5% when targeted appropriately.
• The ensemble diversity defense was broken down to an accuracy of 0% using a combination of PGD and B_IBB attacks.

These outcomes illustrate the breadth of evaluation strategies and the necessity for bespoke attack methodologies tailored to each defense mechanism.

Implications for Future Research

The theoretical and practical implications of this research are profound. Adversarial defenses must be evaluated with a comprehensive and adaptive suite of attacks to genuinely assess robustness. The findings suggest future work should focus on:

• Developing even more nuanced adaptive attacks.
• Considering the potential of hybrid approaches that combine different attack strategies.
• Improving transparency and reproducibility in the evaluation of defenses.

Potential future developments in AI include more robust adversarial training techniques, refined attack methodologies that ensure even distribution of perturbative efforts, and defenses that inherently adapt to the evolving landscape of adaptive attacks.

Conclusion

In conclusion, "On Adaptive Attacks to Adversarial Example Defenses" underscores a fundamental premise: robustly evaluating neural network defenses requires adaptive, carefully tuned attacks. The detailed breakdown of various defenses and the ingenious attack strategies presented in the paper offer a vital resource for researchers aiming to advance the field of adversarial machine learning. Accurate and thorough evaluations are key to developing truly resilient models against adversarial threats.