提出新方法评估模型对微小扰动的鲁棒性，更准更快。
Get Global Guarantees: On the Probabilistic Nature of Perturbation Robustness
- 基于假设检验设计新指标，量化模型在扰动下的概率鲁棒性。
- 相比现有方法，计算效率更高且精度更优，适合部署前评估。
- 适用于自动驾驶等安全关键场景，帮助提升模型可靠性。
在安全关键的深度学习应用中，鲁棒性衡量神经网络应对输入数据中难以察觉的扰动的能力，此类扰动可能引发潜在安全风险。现有的部署前鲁棒性评估方法通常在计算成本与测量精度之间存在显著权衡，限制了其实际应用。为此，本文对现有鲁棒性定义及评估方法进行了全面比较分析，提出了‘塔式鲁棒性’（tower robustness）这一新型实用指标，基于假设检验定量评估概率鲁棒性，实现更严格高效的部署前评估。大量对比实验验证了所提方法的优势与适用性，推动了安全关键深度学习应用中模型鲁棒性的系统性理解与提升。
原文摘要 · Abstract (English)
In safety-critical deep learning applications, robustness measures the ability of neural models that handle imperceptible perturbations in input data, which may lead to potential safety hazards. Existing pre-deployment robustness assessment methods typically suffer from significant trade-offs between computational cost and measurement precision, limiting their practical utility. To address these limitations, this paper conducts a comprehensive comparative analysis of existing robustness definitions and associated assessment methodologies. We propose tower robustness to evaluate robustness, which is a novel, practical metric based on hypothesis testing to quantitatively evaluate probabilistic robustness, enabling more rigorous and efficient pre-deployment assessments. Our extensive comparative evaluation illustrates the advantages and applicability of our proposed approach, thereby advancing the systematic understanding and enhancement of model robustness in safety-critical deep learning applications.
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