# NEUROSEC: FPGA-Based Neuromorphic Audio Security

arXiv: https://arxiv.org/abs/2401.12055 · HTML: https://mlsanity.com/paper/2401.12055/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [Characterizing Audio Adversarial Examples Using Temporal Dependency](https://mlsanity.com/paper/1809.10875/) (2018)
- [DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks](https://mlsanity.com/paper/2107.00415/) (2021)
- [Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement](https://mlsanity.com/paper/2003.13917/) (2020)
- [NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation](https://mlsanity.com/paper/2305.00097/) (2023)
- [Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review](https://mlsanity.com/paper/2202.08897/) (2022)
- [PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment](https://mlsanity.com/paper/2306.15513/) (2023)
- [A Survey of Spiking Neural Network Accelerator on FPGA](https://mlsanity.com/paper/2307.03910/) (2023)
- [A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks](https://mlsanity.com/paper/2204.02942/) (2022)
- [Astrocyte-Integrated Dynamic Function Exchange in Spiking Neural Networks](https://mlsanity.com/paper/2309.08232/) (2023)
- [Exploiting FPGA Capabilities for Accelerated Biomedical Computing](https://mlsanity.com/paper/2307.07914/) (2023)
- [Improving Post-Processing of Audio Event Detectors Using Reinforcement Learning](https://mlsanity.com/paper/2208.09201/) (2022)

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

- [Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Units](https://mlsanity.com/paper/2403.11563/) (2024)
- [Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey](https://mlsanity.com/paper/2601.16589/) (2026)

## Abstract (arXiv)

Neuromorphic systems, inspired by the complexity and functionality of the human brain, have gained interest in academic and industrial attention due to their unparalleled potential across a wide range of applications. While their capabilities herald innovation, it is imperative to underscore that these computational paradigms, analogous to their traditional counterparts, are not impervious to security threats. Although the exploration of neuromorphic methodologies for image and video processing has been rigorously pursued, the realm of neuromorphic audio processing remains in its early stages. Our results highlight the robustness and precision of our FPGA-based neuromorphic system. Specifically, our system showcases a commendable balance between desired signal and background noise, efficient spike rate encoding, and unparalleled resilience against adversarial attacks such as FGSM and PGD. A standout feature of our framework is its detection rate of 94%, which, when compared to other methodologies, underscores its greater capability in identifying and mitigating threats within 5.39 dB, a commendable SNR ratio. Furthermore, neuromorphic computing and hardware security serve many sensor domains in mission-critical and privacy-preserving applications.