论文标题
Roby：通过其决策边界评估深层模型的鲁棒性
ROBY: Evaluating the Robustness of a Deep Model by its Decision Boundaries
论文作者
论文摘要
随着深度学习模型在许多实际任务中的成功应用，模型的鲁棒性变得越来越重要。通常，我们通过使用故意生成的对抗样本攻击深层模型来评估它们的鲁棒性，这在计算上是昂贵的，并且取决于特定的攻击者和模型类型。这项工作提出了一个通用评估公制roby，这是一种基于模型的决策边界的新型攻击鲁棒性措施。 Roby独立于对抗样本，使用类间和阶级统计特征来捕获模型决策边界的特征。我们对十个最新的深层模型进行了实验，并表明Roby与强大的一阶通用攻击者相匹配稳健性的攻击成功率（ASR）。只有1％的时间成本。据我们所知，Roby是第一个轻巧的攻击稳健性评估指标，可以应用于广泛的深层模型。 Roby的代码在https://github.com/baaaad/roby-evaluation-the-robustness-of-a-a-deep-model-by-ist-cisision-decision-boundaries上开放。
With the successful application of deep learning models in many real-world tasks, the model robustness becomes more and more critical. Often, we evaluate the robustness of the deep models by attacking them with purposely generated adversarial samples, which is computationally costly and dependent on the specific attackers and the model types. This work proposes a generic evaluation metric ROBY, a novel attack-independent robustness measure based on the model's decision boundaries. Independent of adversarial samples, ROBY uses the inter-class and intra-class statistic features to capture the features of the model's decision boundaries. We experimented on ten state-of-the-art deep models and showed that ROBY matches the robustness gold standard of attack success rate (ASR) by a strong first-order generic attacker. with only 1% of time cost. To the best of our knowledge, ROBY is the first lightweight attack-independent robustness evaluation metric that can be applied to a wide range of deep models. The code of ROBY is open sourced at https://github.com/baaaad/ROBY-Evaluating-the-Robustness-of-a-Deep-Model-by-its-Decision-Boundaries.
