NEUROSEC: FPGA-Based Neuromorphic Audio Security

• URL: http://arxiv.org/abs/2401.12055v1
• Date: Mon, 22 Jan 2024 15:47:05 GMT
• Title: NEUROSEC: FPGA-Based Neuromorphic Audio Security
• Authors: Murat Isik, Hiruna Vishwamith, Yusuf Sur, Kayode Inadagbo, and I. Can Dikmen
• Abstract summary: This paper highlights the robustness and precision of our FPGA-based neuromorphic system for audio processing. A standout feature of our framework is its detection rate of 94%, which underscores its greater capability in identifying and mitigating threats within 5.39 dB. Neuromorphic computing and hardware security serve many sensor domains in mission-critical and privacy-preserving applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Robust Spiking Neural Networks Against Adversarial Attacks [49.08210314590693]
  Spiking Neural Networks (SNNs) represent a promising paradigm for energy-efficient neuromorphic computing.<n>In this study, we theoretically demonstrate that threshold-neighboring spiking neurons are the key factors limiting the robustness of directly trained SNNs.<n>We find that these neurons set the upper limits for the maximum potential strength of adversarial attacks and are prone to state-flipping under minor disturbances.
  arXiv  Detail & Related papers  (2026-02-24T05:06:12Z)
• General Self-Prediction Enhancement for Spiking Neurons [71.01912385372577]
  Spiking Neural Networks (SNNs) are highly energy-efficient due to event-driven, sparse computation, but their training is challenged by spike non-differentiability and trade-offs among performance, efficiency, and biological plausibility.<n>We propose a self-prediction enhanced spiking neuron method that generates an internal prediction current from its input-output history to modulate membrane potential.<n>This design offers dual advantages, it creates a continuous gradient path that alleviates vanishing gradients and boosts training stability and accuracy, while also aligning with biological principles, which resembles distal dendritic modulation and error-driven synaptic plasticity.
  arXiv  Detail & Related papers  (2026-01-29T15:08:48Z)
• Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey [21.739165659812073]
  Neuromorphic computing mimics brain-inspired mechanisms through spiking neurons and energy-efficient processing.<n>These advancements raise critical security and privacy concerns.<n>This survey systematically analyzes the security landscape of neuromorphic systems.
  arXiv  Detail & Related papers  (2026-01-23T09:43:26Z)
• SteganoSNN: SNN-Based Audio-in-Image Steganography with Encryption [1.3483884526104932]
  This work introduces SteganoSNN, a neuromorphic steganographic framework that exploits spiking neural networks (SNNs) to achieve secure, low-power, and high-capacity multimedia data hiding.<n> Digitised audio samples are converted into spike trains using leaky integrate-and-fire neurons, encrypted via a modulo-based mapping scheme, and embedded into the least significant bits of RGBA image channels.
  arXiv  Detail & Related papers  (2025-11-09T23:31:53Z)
• Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks [57.17129753411926]
  Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs) We propose SpikeSR, which achieves state-of-the-art performance across various remote sensing benchmarks such as AID, DOTA, and DIOR.
  arXiv  Detail & Related papers  (2025-03-06T09:06:06Z)
• On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms [9.071347361654931]
  We assess the assurance of end-to-end fully differentiable neurosymbolic systems that are an emerging method to create data-efficient models. We find end-to-end neurosymbolic methods present unique opportunities for assurance beyond their data efficiency.
  arXiv  Detail & Related papers  (2025-02-13T03:29:42Z)
• Contrastive Learning in Memristor-based Neuromorphic Systems [55.11642177631929]
  Spiking neural networks have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks. In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning.
  arXiv  Detail & Related papers  (2024-09-17T04:48:45Z)
• A Neuromorphic Approach to Obstacle Avoidance in Robot Manipulation [16.696524554516294]
  We develop a neuromorphic approach to obstacle avoidance on a camera-equipped manipulator. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN. Our results motivate incorporating SNN learning, utilizing neuromorphic processors, and further exploring the potential of neuromorphic methods.
  arXiv  Detail & Related papers  (2024-04-08T20:42:10Z)
• Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons [9.404025805661947]
  We introduce an effective method designed to simultaneously enhance adversarial robustness and execution efficiency. Unlike prior studies that enhance robustness via uniformly injecting noise, we introduce a non-uniform noise injection algorithm. By employing approximation techniques, our approach identifies and protects essential neurons while strategically introducing noise into non-essential neurons.
  arXiv  Detail & Related papers  (2024-02-06T19:09:32Z)
• Shielding the Unseen: Privacy Protection through Poisoning NeRF with Spatial Deformation [59.302770084115814]
  We introduce an innovative method of safeguarding user privacy against the generative capabilities of Neural Radiance Fields (NeRF) models. Our novel poisoning attack method induces changes to observed views that are imperceptible to the human eye, yet potent enough to disrupt NeRF's ability to accurately reconstruct a 3D scene. We extensively test our approach on two common NeRF benchmark datasets consisting of 29 real-world scenes with high-quality images.
  arXiv  Detail & Related papers  (2023-10-04T19:35:56Z)
• Neuromorphic Auditory Perception by Neural Spiketrum [27.871072042280712]
  We introduce a neural spike coding model called spiketrumtemporal, to transform the time-varying analog signals into efficient spike patterns. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks.
  arXiv  Detail & Related papers  (2023-09-11T13:06:19Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems [50.101188703826686]
  We present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia.
  arXiv  Detail & Related papers  (2023-04-10T15:12:09Z)
• Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining [24.641328814546842]
  We present Neuro-BERT, a self-supervised pre-training framework of neurological signals based on masked autoencoding in the Fourier domain. We propose a novel pre-training task dubbed Fourier Inversion Prediction (FIP), which randomly masks out a portion of the input signal and then predicts the missing information. By evaluating our method on several benchmark datasets, we show that Neuro-BERT improves downstream neurological-related tasks by a large margin.
  arXiv  Detail & Related papers  (2022-04-20T16:48:18Z)
• Deep Real-Time Decoding of bimanual grip force from EEG & fNIRS [3.0176686218359694]
  We show a way to achieve continuous hand force decoding using cortical signals obtained with non-invasive mobile brain imaging. Our results show a way to achieve continuous hand force decoding using cortical signals obtained with non-invasive mobile brain imaging has immediate impact for rehabilitation, restoration and consumer applications.
  arXiv  Detail & Related papers  (2021-03-09T10:28:05Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting [135.0863818867184]
  artificial neural variability (ANV) helps artificial neural networks learn some advantages from natural'' neural networks. ANV plays as an implicit regularizer of the mutual information between the training data and the learned model. It can effectively relieve overfitting, label noise memorization, and catastrophic forgetting at negligible costs.
  arXiv  Detail & Related papers  (2020-11-12T06:06:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.