# Closing the Gap: Achieving Better Accuracy-Robustness Tradeoffs against Query-Based Attacks Source: [Closing the Gap: Achieving Better Accuracy-Robustness Tradeoffs against Query-Based Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:31:06.502Z Authors: Pascal Zimmer, S\\'ebastien Andreina, Giorgia Azzurra Marson, Ghassan Karame Published: 2023-12-15T17:02:19.000Z ## Abstract Although promising, existing defenses against query-based attacks share a common limitation: they offer increased robustness against attacks at the price of a considerable accuracy drop on clean samples. In this work, we show how to efficiently establish, at test-time, a solid tradeoff between robustness and accuracy when mitigating query-based attacks. Given that these attacks necessarily explore low-confidence regions, our insight is that activating dedicated defenses, such as random noise defense and random image transformations, only for low-confidence inputs is sufficient to prevent them. Our approach is independent of training and supported by theory. We verify the effectiveness of our approach for various existing defenses by conducting extensive experiments on CIFAR-10, CIFAR-100, and ImageNet. Our results confirm that our proposal can indeed enhance these defenses by providing better tradeoffs between robustness and accuracy when compared to state-of-the-art approaches while being completely training-free. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()