Detecting Adversarial Attacks In Time-Series Data

In recent times, deep neural networks have seen increased adoption in highly critical tasks. They are also susceptible to adversarial attacks, which are specifically crafted changes made to input samples which lead to erroneous output from such models. Such attacks have been shown to affect different types of data such as images and more recently, time-series data. Such susceptibility could have catastrophic consequences, depending on the domain. We propose a method for detecting Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) adversarial attacks as adapted for time-series data. We frame the problem as an instance of outlier detection and construct a normalcy model based on information and chaos-theoretic measures, which can then be used to determine whether unseen samples are normal or adversarial. Our approach shows promising performance on several datasets from the 2015 UCR Time Series Archive, reaching up to 97% detection accuracy in the best case.