探索对抗前沿：通过对抗超体积量化鲁棒性
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
AI总结：
针对传统对抗准确率无法全面衡量模型鲁棒性的问题，提出对抗超体积指标从多目标优化角度全面评估扰动强度范围内的鲁棒性，并采用新训练算法均匀增强鲁棒性，为防御模型评估建立新标准。
摘要：
深度学习模型面临的对抗攻击威胁日益加剧，特别是在安全关键领域，这凸显了对鲁棒深度学习系统的需求。传统的鲁棒性评估依赖于对抗准确率，即在特定扰动强度下衡量模型的性能。然而，这一单一指标无法完全概括模型在不同扰动程度下的整体韧性。为了弥补这一不足，我们提出了一种名为对抗超体积（adversarial hypervolume）的新指标，从多目标优化的角度，在一系列扰动强度上全面评估深度学习模型的鲁棒性。该指标能够深入比较各种防御机制，并识别出较弱的防御策略所提供的微不足道的鲁棒性提升。此外，我们采用了一种新颖的训练算法，能够在各种扰动强度上均匀地增强对抗鲁棒性，而不同于那些仅狭隘地专注于优化对抗准确率的方法。我们广泛的经验研究验证了对抗超体积指标的有效性，证明了其能够揭示对抗准确率所忽略的鲁棒性方面的细微差异。本研究贡献了一种新的鲁棒性度量方法，并为评估和基准测试当前及未来防御模型抵御对抗威胁的韧性建立了标准。
英文摘要：
The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial accuracy, which measures a model's performance under a specific perturbation intensity. However, this singular metric does not fully encapsulate the overall resilience of a model against varying degrees of perturbation. To address this gap, we propose a new metric termed adversarial hypervolume, assessing the robustness of deep learning models comprehensively over a range of perturbation intensities from a multi-objective optimization standpoint. This metric allows for an in-depth comparison of defense mechanisms and recognizes the trivial improvements in robustness afforded by less potent defensive strategies. Additionally, we adopt a novel training algorithm that enhances adversarial robustness uniformly across various perturbation intensities, in contrast to methods narrowly focused on optimizing adversarial accuracy. Our extensive empirical studies validate the effectiveness of the adversarial hypervolume metric, demonstrating its ability to reveal subtle differences in robustness that adversarial accuracy overlooks. This research contributes a new measure of robustness and establishes a standard for assessing and benchmarking the resilience of current and future defensive models against adversarial threats.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
