# Universal Fourier Attack for Time Series

arXiv: https://arxiv.org/abs/2209.00757 · HTML: https://mlsanity.com/paper/2209.00757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors](https://mlsanity.com/paper/1910.14667/) (2019)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Robust Audio Adversarial Example for a Physical Attack](https://mlsanity.com/paper/1810.11793/) (2018)
- [WaveGuard: Understanding and Mitigating Audio Adversarial Examples](https://mlsanity.com/paper/2103.03344/) (2021)
- [Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications](https://mlsanity.com/paper/1903.01563/) (2019)
- [Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition](https://mlsanity.com/paper/1812.10061/) (2018)
- [Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition](https://mlsanity.com/paper/1809.04397/) (2018)
- [Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness](https://mlsanity.com/paper/1902.06044/) (2019)
- [Real-Time Neural Voice Camouflage](https://mlsanity.com/paper/2112.07076/) (2021)
- [Adversarial Example Devastation and Detection on Speech Recognition System by Adding Random Noise](https://mlsanity.com/paper/2108.13562/) (2021)
- [Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations](https://mlsanity.com/paper/2010.12809/) (2020)

## Built on by (most cited first, 1 of 1)

- [The Dark Side of Digital Twins: Adversarial Attacks on AI-Driven Water Forecasting](https://mlsanity.com/paper/2504.20295/) (2025)

## Abstract (arXiv)

A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines.