NEUROSEC: FPGA-Based Neuromorphic Audio Security
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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.
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
11 of 27 references.
References · 11
- 2018Characterizing Audio Adversarial Examples Using Temporal Dependencycited by 36
- 2021DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networkscited by 21
- 2020Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancementcited by 18
- 2023NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscationcited by 16
- 2022Implementing Spiking Neural Networks on Neuromorphic Architectures: A Reviewcited by 15
- 2023PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deploymentcited by 7
- 2023A Survey of Spiking Neural Network Accelerator on FPGAcited by 7
- 2022A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networkscited by 4
- 2023Astrocyte-Integrated Dynamic Function Exchange in Spiking Neural Networkscited by 3
- 2023Exploiting FPGA Capabilities for Accelerated Biomedical Computingcited by 2
- 2022Improving Post-Processing of Audio Event Detectors Using Reinforcement Learningcited by 1
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.