Adversarial examples, or nearly indistinguishable inputs created by an attacker, significantly reduce machine learning accuracy. Theoretical evidence has shown that the high intrinsic dimensionality of datasets facilitates an adversary's ability to develop effective adversarial examples in classification models. Adjacently, the presentation of data to a learning model impacts its performance. For example, we have seen this through dimensionality reduction techniques used to aid with the generalization of features in machine learning applications. Thus, data transformation techniques go hand-in-hand with state-of-the-art learning models in decision-making applications such as intelligent medical or military systems. With this work, we explore how data transformations techniques such as feature selection, dimensionality reduction, or trend extraction techniques may impact an adversary's ability to create effective adversarial samples on a recurrent neural network. Specifically, we analyze it from the perspective of the data manifold and the presentation of its intrinsic features. Our evaluation empirically shows that feature selection and trend extraction techniques may increase the RNN's vulnerability. A data transformation technique reduces the vulnerability to adversarial examples only if it approximates the dataset's intrinsic dimension, minimizes codimension, and maintains higher manifold coverage.

翻译：理论证据表明,数据集的高度内在维度有助于对手开发在分类模型中有效的对抗性实例的能力。 相近地,数据向学习模型的展示会影响其性能。例如,我们通过用于协助机械学习应用特征的概括化应用特征的维度减少技术,看到了这一点。因此,数据转换技术与智能医疗或军事系统等决策应用中最先进的学习模型携手并进。在这项工作中,我们探索了诸如特征选择、维度减少或趋势提取技术等数据转换技术如何影响对手在经常性神经网络上创建有效对抗性样本的能力。具体地说,我们从数据组合的角度分析这些数据,并介绍其内在特征。我们的评估经验表明,特征选择和趋势提取技术可能会增加RNN的脆弱程度。数据转换技术只有在接近数据集的内在层面时,才能降低对更高对抗性实例的脆弱性。