# A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards

arXiv: https://arxiv.org/abs/2308.01074 · HTML: https://mlsanity.com/paper/2308.01074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [CoAtNet: Marrying Convolution and Attention for All Data Sizes](https://mlsanity.com/paper/2106.04803/) (2021)
- [Don't Skype & Type! Acoustic Eavesdropping in Voice-Over-IP](https://mlsanity.com/paper/1609.09359/) (2016)
- [Hearing your touch: A new acoustic side channel on smartphones](https://mlsanity.com/paper/1903.11137/) (2019)
- [Stealing PINs via Mobile Sensors: Actual Risk versus User Perception](https://mlsanity.com/paper/1605.05549/) (2016)
- [TouchSignatures: Identification of User Touch Actions and PINs Based on Mobile Sensor Data via JavaScript](https://mlsanity.com/paper/1602.04115/) (2016)

## Abstract (arXiv)

With recent developments in deep learning, the ubiquity of micro-phones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat to keyboards than ever. This paper presents a practical implementation of a state-of-the-art deep learning model in order to classify laptop keystrokes, using a smartphone integrated microphone. When trained on keystrokes recorded by a nearby phone, the classifier achieved an accuracy of 95%, the highest accuracy seen without the use of a language model. When trained on keystrokes recorded using the video-conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium. Our results prove the practicality of these side channel attacks via off-the-shelf equipment and algorithms. We discuss a series of mitigation methods to protect users against these series of attacks.