Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features
Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, little is known about their robustness to adversarial attacks. In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-STAT)}. To address the unique challenges of time-series domain, TSA-STAT employs constraints on statistical features of the time-series data to construct adversarial examples. Optimized polynomial transformations are used to create attacks that are more effective (in terms of successfully fooling DNNs) than those based on additive perturbations. We also provide certified bounds on the norm of the statistical features for constructing adversarial examples. Our experiments on diverse real-world benchmark datasets show the effectiveness of TSA-STAT in fooling DNNs for time-series domain and in improving their robustness. The source code of TSA-STAT algorithms is available at https://github.com/tahabelkhouja/Time-Series-Attacks-via-STATistical-Features
Code (1)
Tasks
Time SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Fortifying Time Series: DTW-Certified Robust Anomaly Detection
Time-series anomaly detection is critical for ensuring safety in high-stakes applications, where robustness is a fundamental requirement rather than a mere performance metric. Addressing the vulnerability of these system…
Anomaly DetectionWasserstein Adversarial Examples on Univariant Time Series Data
Adversarial examples are crafted by adding indistinguishable perturbations to normal examples in order to fool a well-trained deep learning model to misclassify. In the context of computer vision, this notion of indistin…
Adversarial AttackTime SeriesLearning Better Certified Models from Empirically-Robust Teachers
Adversarial training attains strong empirical robustness to specific adversarial attacks by training on concrete adversarial perturbations, but it produces neural networks that are not amenable to strong robustness certi…
Knowledge DistillationEfficient Certified Training and Robustness Verification of Neural ODEs
Neural Ordinary Differential Equations (NODEs) are a novel neural architecture, built around initial value problems with learned dynamics which are solved during inference. Thought to be inherently more robust against ad…
Time SeriesTime Series AnalysisTime Series ForecastingCertified Adversarial Robustness Under the Bounded Support Set
Deep neural networks (DNNs) have revealed severe vulnerability to adversarial perturbations, beside empirical adversarial training for robustness, the design of provably robust classifiers attracts more and more attentio…
Adversarial Robustness