Closing the Gap: Achieving Better Accuracy-Robustness Tradeoffs against Query-Based Attacks

Core Concepts

Establishing a strong tradeoff between robustness and accuracy when mitigating query-based attacks by activating defenses based on confidence levels.

Abstract

Existing defenses against query-based attacks offer increased robustness at the expense of accuracy on clean samples.
Proposal suggests activating defenses only for low-confidence inputs to prevent attacks.
Approach verified through experiments on CIFAR-10, CIFAR-100, and ImageNet datasets.
Results show improved tradeoffs between robustness and accuracy compared to state-of-the-art approaches.

Stats

"Our approach relies on two main parameters: the confidence threshold τ and the genuine defense’s parameter ν."
"For RND defense with noise ν = 0.05, CA increases from 82% when τ ≥0.99 to 87% when τ = 0.97."
"RA starts at 71% for the baseline, remains consistent at 71% when τ = 0.99, and decreases to 64% when τ = 0.97."

Quotes

"Our insight is that obstructing the generation of low-confidence adversarial inputs is sufficient to thwart the exact location of boundary samples."
"Existing defenses share a common limitation affecting benign samples similarly to adversarial examples."
"Our proposal outperforms all training/inference-time-based defenses against SurFree."

Key Insights Distilled From

Closing the Gap

by Pasc... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2312.10132.pdf

Deeper Inquiries

How can this approach be adapted for real-world applications beyond image classification

This approach can be adapted for real-world applications beyond image classification by extending the concept of activating defenses based on confidence levels to other domains where machine learning models are vulnerable to adversarial attacks. For example, in natural language processing tasks such as sentiment analysis or text classification, the model could activate defense mechanisms like adding random noise or perturbations only when it is uncertain about its predictions. This would help in mitigating adversarial attacks that aim to manipulate the model's output by exploiting areas of low confidence. Similarly, in cybersecurity applications like intrusion detection systems or malware detection, this approach could be used to enhance the robustness of models against adversarial attacks.

What counterarguments exist against activating defenses based on confidence levels

Counterarguments against activating defenses based on confidence levels may include concerns about potential misclassification of genuine samples with low confidence and the impact on overall model performance. By activating defenses only for inputs with low-confidence predictions, there is a risk of increasing false positives and false negatives for genuine samples that fall into these regions. This could lead to a decrease in accuracy on clean data and potentially affect the usability and reliability of the model in real-world scenarios. Additionally, adversaries might adapt their attack strategies to target high-confidence regions instead, rendering this defense mechanism less effective against sophisticated attackers.

How does model calibration impact the effectiveness of this approach

Model calibration plays a crucial role in determining the effectiveness of this approach by ensuring that predicted confidences align well with actual accuracies. Properly calibrated models provide more reliable estimates of their own uncertainty levels, which are essential for accurately identifying low-confidence regions where defenses should be activated. If a model is poorly calibrated and consistently underestimates its uncertainties (overconfident predictions), it may fail to trigger defenses when needed or activate them unnecessarily on high-confidence inputs, leading to suboptimal tradeoffs between robustness and accuracy. Therefore, accurate calibration helps optimize the threshold selection process and enhances the overall performance of this approach.