# Characterizing Audio Adversarial Examples Using Temporal Dependency

arXiv: https://arxiv.org/abs/1809.10875 · HTML: https://mlsanity.com/paper/1809.10875/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 30 of 36)

- [Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models](https://mlsanity.com/paper/2111.02840/) (2021)
- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Structure Invariant Transformation for better Adversarial Transferability](https://mlsanity.com/paper/2309.14700/) (2023)
- [WaveGuard: Understanding and Mitigating Audio Adversarial Examples](https://mlsanity.com/paper/2103.03344/) (2021)
- [Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information](https://mlsanity.com/paper/2110.09714/) (2021)
- [Can audio-visual integration strengthen robustness under multimodal attacks?](https://mlsanity.com/paper/2104.02000/) (2021)
- [Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition](https://mlsanity.com/paper/2206.03393/) (2022)
- [Defending against Adversarial Audio via Diffusion Model](https://mlsanity.com/paper/2303.01507/) (2023)
- [Sequential Randomized Smoothing for Adversarially Robust Speech Recognition](https://mlsanity.com/paper/2112.03000/) (2021)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [Holistic Adversarial Robustness of Deep Learning Models](https://mlsanity.com/paper/2202.07201/) (2022)
- [Dompteur: Taming Audio Adversarial Examples](https://mlsanity.com/paper/2102.05431/) (2021)
- [AI Maintenance: A Robustness Perspective](https://mlsanity.com/paper/2301.03052/) (2023)
- [PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection](https://mlsanity.com/paper/2309.06960/) (2023)
- [SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems](https://mlsanity.com/paper/2103.10651/) (2021)
- [Transferable Adversarial Attacks against ASR](https://mlsanity.com/paper/2411.09220/) (2024)
- [SEC4SR: A Security Analysis Platform for Speaker Recognition](https://mlsanity.com/paper/2109.01766/) (2021)
- [On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems](https://mlsanity.com/paper/2202.05725/) (2022)
- [DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution](https://mlsanity.com/paper/2305.17000/) (2023)
- [Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models](https://mlsanity.com/paper/2409.07936/) (2024)
- [Hijacking Large Audio-Language Models via Context-Agnostic and Imperceptible Auditory Prompt Injection](https://mlsanity.com/paper/2604.14604/) (2026)
- [Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition](https://mlsanity.com/paper/2202.08532/) (2022)
- [Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models](https://mlsanity.com/paper/2209.13523/) (2022)
- [NEUROSEC: FPGA-Based Neuromorphic Audio Security](https://mlsanity.com/paper/2401.12055/) (2024)
- [Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer](https://mlsanity.com/paper/2405.09470/) (2024)
- [Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition Systems](https://mlsanity.com/paper/2504.00858/) (2025)
- [Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking](https://mlsanity.com/paper/2112.01821/) (2021)
- [Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise](https://mlsanity.com/paper/2211.01621/) (2022)
- [MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems](https://mlsanity.com/paper/2303.05758/) (2023)
- [Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence](https://mlsanity.com/paper/2112.13060/) (2021)

## Abstract (arXiv)

Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream applications. Nonetheless, as unique data properties have inspired distinct and powerful learning principles, this paper aims to explore their potentials towards mitigating adversarial inputs. In particular, our results reveal the importance of using the temporal dependency in audio data to gain discriminate power against adversarial examples. Tested on the automatic speech recognition (ASR) tasks and three recent audio adversarial attacks, we find that (i) input transformation developed from image adversarial defense provides limited robustness improvement and is subtle to advanced attacks; (ii) temporal dependency can be exploited to gain discriminative power against audio adversarial examples and is resistant to adaptive attacks considered in our experiments. Our results not only show promising means of improving the robustness of ASR systems, but also offer novel insights in exploiting domain-specific data properties to mitigate negative effects of adversarial examples.