Lightweight Defense Against Adversarial Attacks in Time Series Classification
This work addresses robust time series classification for data mining applications, offering incremental improvements over existing adversarial training methods.
The paper tackled the problem of adversarial attacks in time series classification by developing lightweight data augmentation-based defense methods, with the most intensive method increasing computational resources by only 14.07% and an ensemble method requiring less than one-third the resources of PGD-based adversarial training while providing better defense performance.
As time series classification (TSC) gains prominence, ensuring robust TSC models against adversarial attacks is crucial. While adversarial defense is well-studied in Computer Vision (CV), the TSC field has primarily relied on adversarial training (AT), which is computationally expensive. In this paper, five data augmentation-based defense methods tailored for time series are developed, with the most computationally intensive method among them increasing the computational resources by only 14.07% compared to the original TSC model. Moreover, the deployment process for these methods is straightforward. By leveraging these advantages of our methods, we create two combined methods. One of these methods is an ensemble of all the proposed techniques, which not only provides better defense performance than PGD-based AT but also enhances the generalization ability of TSC models. Moreover, the computational resources required for our ensemble are less than one-third of those required for PGD-based AT. These methods advance robust TSC in data mining. Furthermore, as foundation models are increasingly explored for time series feature learning, our work provides insights into integrating data augmentation-based adversarial defense with large-scale pre-trained models in future research.