通过数据相似性选择最优模型，81%降计算开销且抗攻击。
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks
- 基于数据相似性匹配，选最适配的预训练模型。
- 平均降低81.2%计算开销，性能仅比理想值低4.2%。
- 适合资源受限场景下的时间序列抗攻击分类任务。
在时间序列分类中，尤其是深度学习模型，降低计算开销是一个重大挑战，而对抗攻击进一步加剧了这一问题，因此需要兼具鲁棒性与高效性的方法。本文提出ReLATE框架，通过基于数据相似性的方法识别鲁棒学习器，减少计算开销并提升韧性。ReLATE在已知的对抗攻击场景下维护多个深度学习模型，捕捉其性能表现；利用相似性度量找到与目标数据集最相似的数据集，进而采用该数据集上表现最优的模型。实验表明，ReLATE平均降低81.2%的计算开销，在不牺牲性能的前提下，达到接近理想（Oracle）水平的4.2%以内。
原文摘要 · Abstract (English)
Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge. This challenge is further compounded by adversarial attacks, emphasizing the need for resilient methods that ensure robust performance and efficient model selection. We introduce ReLATE, a framework that identifies robust learners based on dataset similarity, reduces computational overhead, and enhances resilience. ReLATE maintains multiple deep learning models in well-known adversarial attack scenarios, capturing model performance. ReLATE identifies the most analogous dataset to a given target using a similarity metric, then applies the optimal model from the most similar dataset. ReLATE reduces computational overhead by an average of 81.2%, enhancing adversarial resilience and streamlining robust model selection, all without sacrificing performance, within 4.2% of Oracle.
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