# Open Set RF Fingerprinting using Generative Outlier Augmentation

arXiv: https://arxiv.org/abs/2108.13099 · HTML: https://mlsanity.com/paper/2108.13099/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations](https://mlsanity.com/paper/2005.00954/) (2020)
- [Wireless Physical-Layer Identification: Modeling and Validation](https://mlsanity.com/paper/1510.08485/) (2015)
- [Deep Learning Approaches for Open Set Wireless Transmitter Authorization](https://mlsanity.com/paper/2002.07777/) (2020)

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

- [HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication](https://mlsanity.com/paper/2305.09594/) (2023)
- [Out-of-Distribution (OOD) Detectors for Open-Set RF Fingerprinting](https://mlsanity.com/paper/2606.12718/) (2026)

## Abstract (arXiv)

RF devices can be identified by unique imperfections embedded in the signals they transmit called RF fingerprints. The closed set classification of such devices, where the identification must be made among an authorized set of transmitters, has been well explored. However, the much more difficult problem of open set classification, where the classifier needs to reject unauthorized transmitters while recognizing authorized transmitters, has only been recently visited. So far, efforts at open set classification have largely relied on the utilization of signal samples captured from a known set of unauthorized transmitters to aid the classifier learn unauthorized transmitter fingerprints. Since acquiring new transmitters to use as known transmitters is highly expensive, we propose to use generative deep learning methods to emulate unauthorized signal samples for the augmentation of training datasets. We develop two different data augmentation techniques, one that exploits a limited number of known unauthorized transmitters and the other that does not require any unauthorized transmitters. Experiments conducted on a dataset captured from a WiFi testbed indicate that data augmentation allows for significant increases in open set classification accuracy, especially when the authorized set is small.