# Adversarial Transferability in Wearable Sensor Systems

arXiv: https://arxiv.org/abs/2003.07982 · HTML: https://mlsanity.com/paper/2003.07982/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Who's Afraid of Adversarial Transferability?](https://mlsanity.com/paper/2105.00433/) (2021)
- [Beyond Attack Success Rate: A Multi-Metric Evaluation of Adversarial Transferability in Medical Imaging Models](https://mlsanity.com/paper/2604.16532/) (2026)

## Abstract (arXiv)

Machine learning is used for inference and decision making in wearable sensor systems. However, recent studies have found that machine learning algorithms are easily fooled by the addition of adversarial perturbations to their inputs. What is more interesting is that adversarial examples generated for one machine learning system is also effective against other systems. This property of adversarial examples is called transferability. In this work, we take the first stride in studying adversarial transferability in wearable sensor systems from the following perspectives: 1) transferability between machine learning systems, 2) transferability across subjects, 3) transferability across sensor body locations, and 4) transferability across datasets. We found strong untargeted transferability in most cases. Targeted attacks were less successful with success scores from 0% to 80%. The transferability of adversarial examples depends on many factors such as the inclusion of data from all subjects, sensor body position, number of samples in the dataset, type of learning algorithm, and the distribution of source and target system dataset. The transferability of adversarial examples decreases sharply when the data distribution of the source and target system becomes more distinct. We also provide guidelines for the community for designing robust sensor systems.