Abstract
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.
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References
Bu, T., Ding, J., Hao, Z., Yu, Z.: Rate gradient approximation attack threats deep spiking neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7896–7906 (2023)
Chen, X., Li, S., Huang, H.: Adversarial attack and defense on deep neural network-based voice processing systems: an overview. Appl. Sci. 11(18), 8450 (2021)
Galán, D.G.: Neuromorphic auditory computing: towards a digital, event-based implementation of the hearing sense for robotics. Ph.D. thesis, Universidad de Sevilla (2022)
Giannakopoulos, P., Pikrakis, A., Cotronis, Y.: Improving post-processing of audio event detectors using reinforcement learning. IEEE Access 10, 84398–84404 (2022)
Gongye, C., Luo, Y., Xu, X., Fei, Y.: HammerDodger: a lightweight defense framework against RowHammer attack on DNNs. In: 2023 60th ACM/IEEE Design Automation Conference (DAC), pp. 1–6. IEEE (2023)
Huynh, P.K., Varshika, M.L., Paul, A., Isik, M., Balaji, A., Das, A.: Implementing spiking neural networks on neuromorphic architectures: a review. arXiv preprint arXiv:2202.08897 (2022)
Inadagbo, K., Arig, B., Alici, N., Isik, M.: Exploiting FPGA capabilities for accelerated biomedical computing. In: 2023 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 48–53. IEEE (2023)
Isik, M.: A survey of spiking neural network accelerator on FPGA. arXiv preprint arXiv:2307.03910 (2023)
Isik, M., Inadagbo, K.: Astrocyte-integrated dynamic function exchange in spiking neural networks. In: Kofroň, J., Margaria, T., Seceleanu, C. (eds.) International Conference on Engineering of Computer-Based Systems, pp. 263–273. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-49252-5_24
Isik, M., Paul, A., Varshika, M.L., Das, A.: A design methodology for fault-tolerant computing using astrocyte neural networks. In: Proceedings of the 19th ACM International Conference on Computing Frontiers, pp. 169–172 (2022)
Jia, X., et al.: XVDPU: a high performance CNN accelerator on the Versal platform powered by the AI engine. In: 2022 32nd International Conference on Field-Programmable Logic and Applications (FPL), pp. 01–09. IEEE (2022)
Kwon, H., Yoon, H., Park, K.W.: Poster: detecting audio adversarial example through audio modification. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 2521–2523 (2019)
Liu, B., Yang, C., Li, H., Chen, Y., Wu, Q., Barnell, M.: Security of neuromorphic systems: challenges and solutions. In: 2016 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1326–1329. IEEE (2016)
Madden, K., Harkin, J., McDaid, L., Nugent, C.: Adding security to networks-on-chip using neural networks. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1299–1306. IEEE (2018)
Maji, S., Lee, K., Gongye, C., Fei, Y., Chandrakasan, A.P.: An energy-efficient neural network accelerator with improved protections against fault-attacks. In: ESSCIRC 2023- IEEE 49th European Solid State Circuits Conference (ESSCIRC). pp. 233–236 (2023). https://doi.org/10.1109/ESSCIRC59616.2023.10268746
Marchisio, A., Pira, G., Martina, M., Masera, G., Shafique, M.: DVS-attacks: adversarial attacks on dynamic vision sensors for spiking neural networks. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–9. IEEE (2021)
Merchant, F.: Security as an important ingredient in neuromorphic engineering. In: 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pp. 314–319. IEEE (2022)
Peng, H., et al.: PASNet: polynomial architecture search framework for two-party computation-based secure neural network deployment. In: 2023 60th ACM/IEEE Design Automation Conference (DAC), pp. 1–6. IEEE (2023)
Perryman, N., Wilson, C., George, A.: Evaluation of Xilinx Versal architecture for next-gen edge computing in space. In: 2023 IEEE Aerospace Conference, pp. 1–11. IEEE (2023)
Salehi, S., et al.: Neuromorphic-enabled security for IoT. In: 2022 20th IEEE Interregional NEWCAS Conference (NEWCAS), pp. 153–157. IEEE (2022)
Sepulveda, J., Reinbrecht, C., Diguet, J.P.: Security aspects of neuromorphic MPSoCs. In: 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 1–6. IEEE (2018)
Staudigl, F., et al.: Fault injection in native logic-in-memory computation on neuromorphic hardware. arXiv preprint arXiv:2302.07655 (2023)
Tayarani-Najaran, M.H., Schmuker, M.: Event-based sensing and signal processing in the visual, auditory, and olfactory domain: a review. Front. Neural Circuits 15, 610446 (2021)
Yang, C.H., Qi, J., Chen, P.Y., Ma, X., Lee, C.H.: Characterizing speech adversarial examples using self-attention U-Net enhancement. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3107–3111. IEEE (2020)
Yang, Z., Li, B., Chen, P.Y., Song, D.: Characterizing audio adversarial examples using temporal dependency. arXiv preprint arXiv:1809.10875 (2018)
Zhou, T., Luo, Y., Ren, S., Xu, X.: NNSplitter: an active defense solution to DNN model via automated weight obfuscation. arXiv preprint arXiv:2305.00097 (2023)
Acknowledgment
We acknowledge the Temsa Research R&D Center for their generous financial support and the reviewers for their invaluable insights and suggestions that significantly contributed to the enhancement of our paper.
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Isik, M., Vishwamith, H., Sur, Y., Inadagbo, K., Dikmen, I.C. (2024). NEUROSEC: FPGA-Based Neuromorphic Audio Security. In: Skliarova, I., Brox Jiménez, P., Véstias, M., Diniz, P.C. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2024. Lecture Notes in Computer Science, vol 14553. Springer, Cham. https://doi.org/10.1007/978-3-031-55673-9_10
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