GENADA: efficient generative time series adversarial attack framework

2026-08-12

AI Summary

This work addresses the vulnerability of deep learning models for time series to adversarial attacks and the high computational cost of existing gradient-based attack methods. To this end, the authors propose GENADA, a novel framework that introduces generative modeling into time series adversarial attacks for the first time. Built upon generative adversarial networks, GENADA efficiently produces adversarial perturbations in a single forward pass and supports both single-step and iterative attack strategies. Experimental results demonstrate that GENADA achieves attack effectiveness comparable to strong baselines across multiple datasets and models, while substantially reducing the time required for perturbation generation and significantly improving inference efficiency.

Abstract

Deep learning models are widely used for time series analysis in domains such as healthcare, finance, energy systems, and environmental monitoring. However, these models remain vulnerable to adversarial attacks, where small input perturbations cause severe degradation in predictive performance. Commonly used gradient-based attacks, iterative first-order methods, are computationally burdensome, as they repeatedly backpropagate through the victim model to compute input gradients during a number of iterative refinement steps. We propose a GENerative ADversarial Attack (GENADA) that learns a generative model to produce deceptive perturbations directly in a single forward pass and a procedure to train it. Variants include single-step and iterative generative attack schemes. The validation considers attacks on several neural models and datasets in the time-series domain, a controlled, low-dimensional setting. Empirically, GENADA achieves comparable attack quality to strong baselines while requiring less time to generate perturbations during inference.

Problem

• adversarial attack
• time series
• deep learning
• computational efficiency
• generative model

Innovation

• generative adversarial attack
• time series
• efficient perturbation generation
• adversarial robustness
• deep learning