Room for Error: Large-Scale Simulation of Over-the-Air Acoustic Attacks
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Abstract (arXiv)
While voice control is rapidly becoming a ubiquitous vector of human-AI communication, the risks facing these systems remain poorly understood. This is, in part, a product of the difficulties in scaling strictly digital adversarial workflows to the physical world. These scale barriers have led the community to abstract away key acoustic factors relating to detectability and the influence of geometry on acoustics. These methodological and metrological shortcomings undermine our understanding of risk. We illuminate these issues through real-world testing, conceptual discussions, and a novel, high-throughput reality simulation framework. By testing over 8 million adversarial evaluations, we demonstrate that acoustic awareness yields relative Word Error Rate increases of up to 94.5% under Whisper and wav2vec. We employ this framework to explore a formalize and operationalize a Dual-Form Signal to Noise Ratio to decouple source stealth from victim attack efficacy, resolving a crucial limitation in current works. This lays the groundwork for repeatable, verifiable research that embraces, rather than abstracts, the acoustic environment.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 26 Jun 2026.
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References · 15
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
- 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
- 2017Pyroomacoustics: A Python package for audio room simulations and array processing algorithmscited by 131
- 2019Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognitioncited by 123
- 2018Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hidingcited by 83
- 2018CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognitioncited by 63
- 2020SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systemscited by 46
- 2018Robust Audio Adversarial Example for a Physical Attackcited by 42
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