Membership Privacy Evaluation in Deep Spiking Neural Networks

• URL: http://arxiv.org/abs/2409.19413v1
• Date: Sat, 28 Sep 2024 17:13:04 GMT
• Title: Membership Privacy Evaluation in Deep Spiking Neural Networks
• Authors: Jiaxin Li, Gorka Abad, Stjepan Picek, Mauro Conti,
• Abstract summary: Spiking Neural Networks (SNNs) mimic neurons with non-linear functions to output floating-point numbers. In this paper, we evaluate the membership privacy of SNNs by considering eight MIAs. We show that SNNs are more vulnerable (maximum 10% higher in terms of balanced attack accuracy) than ANNs when both are trained with neuromorphic datasets.
• Score: 32.42695393291052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Neural Networks (ANNs), commonly mimicking neurons with non-linear functions to output floating-point numbers, consistently receive the same signals of a data point during its forward time. Unlike ANNs, Spiking Neural Networks (SNNs) get various input signals in the forward time of a data point and simulate neurons in a biologically plausible way, i.e., producing a spike (a binary value) if the accumulated membrane potential of a neuron is larger than a threshold. Even though ANNs have achieved remarkable success in multiple tasks, e.g., face recognition and object detection, SNNs have recently obtained attention due to their low power consumption, fast inference, and event-driven properties. While privacy threats against ANNs are widely explored, much less work has been done on SNNs. For instance, it is well-known that ANNs are vulnerable to the Membership Inference Attack (MIA), but whether the same applies to SNNs is not explored. In this paper, we evaluate the membership privacy of SNNs by considering eight MIAs, seven of which are inspired by MIAs against ANNs. Our evaluation results show that SNNs are more vulnerable (maximum 10% higher in terms of balanced attack accuracy) than ANNs when both are trained with neuromorphic datasets (with time dimension). On the other hand, when training ANNs or SNNs with static datasets (without time dimension), the vulnerability depends on the dataset used. If we convert ANNs trained with static datasets to SNNs, the accuracy of MIAs drops (maximum 11.5% with a reduction of 7.6% on the test accuracy of the target model). Next, we explore the impact factors of MIAs on SNNs by conducting a hyperparameter study. Finally, we show that the basic data augmentation method for static data and two recent data augmentation methods for neuromorphic data can considerably (maximum reduction of 25.7%) decrease MIAs' performance on SNNs.

Related papers

• Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks [50.32980443749865]
  Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biologicalability. Current SNNs struggle to balance accuracy and latency in neuromorphic datasets. We propose Step-wise Distillation (HSD) method, tailored for neuromorphic datasets.
  arXiv  Detail & Related papers  (2024-09-19T06:52:34Z)
• NAS-BNN: Neural Architecture Search for Binary Neural Networks [55.058512316210056]
  We propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN. Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M. In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS dataset.
  arXiv  Detail & Related papers  (2024-08-28T02:17:58Z)
• SEENN: Towards Temporal Spiking Early-Exit Neural Networks [26.405775809170308]
  Spiking Neural Networks (SNNs) have recently become more popular as a biologically plausible substitute for traditional Artificial Neural Networks (ANNs) We study a fine-grained adjustment of the number of timesteps in SNNs. By dynamically adjusting the number of timesteps, our SEENN achieves a remarkable reduction in the average number of timesteps during inference.
  arXiv  Detail & Related papers  (2023-04-02T15:57:09Z)
• Low Latency Conversion of Artificial Neural Network Models to Rate-encoded Spiking Neural Networks [11.300257721586432]
  Spiking neural networks (SNNs) are well suited for resource-constrained applications. In a typical rate-encoded SNN, a series of binary spikes within a globally fixed time window is used to fire the neurons. The aim of this paper is to reduce this while maintaining accuracy when converting ANNs to their equivalent SNNs.
  arXiv  Detail & Related papers  (2022-10-27T08:13:20Z)
• Attention Spiking Neural Networks [32.591900260554326]
  We study the effect of attention mechanisms in spiking neural networks (SNNs) New attention SNN architecture with end-to-end training called "MA-SNN" is proposed. Experiments are conducted in event-based DVS128 Gesture/Gait action recognition and ImageNet-1k image classification.
  arXiv  Detail & Related papers  (2022-09-28T09:00:45Z)
• Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics [6.309365332210523]
  Spiking Neural Networks (SNNs) with their neuro-inspired event-driven processing can efficiently handle asynchronous data. We propose an adaptive fully-spiking framework with learnable neuronal dynamics to alleviate the spike vanishing problem. Our experiments on datasets show an average reduction of 13% in average endpoint error (AEE) compared to state-of-the-art ANNs.
  arXiv  Detail & Related papers  (2022-09-21T21:17:56Z)
• SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks [117.56823277328803]
  Spiking neural networks are efficient computation models for low-power environments. We propose a SNN-to-ANN (SNN2ANN) framework to train the SNN in a fast and memory-efficient way. Experiment results show that our SNN2ANN-based models perform well on the benchmark datasets.
  arXiv  Detail & Related papers  (2022-06-19T16:52:56Z)
• Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [70.75043144299168]
  Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. It is a challenge to efficiently train SNNs due to their non-differentiability. We propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance.
  arXiv  Detail & Related papers  (2022-05-01T12:44:49Z)
• Toward Robust Spiking Neural Network Against Adversarial Perturbation [22.56553160359798]
  spiking neural networks (SNNs) are deployed increasingly in real-world efficiency critical applications. Researchers have already demonstrated an SNN can be attacked with adversarial examples. To the best of our knowledge, this is the first analysis on robust training of SNNs.
  arXiv  Detail & Related papers  (2022-04-12T21:26:49Z)
• Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning [60.20150317299749]
  This paper proposes a deep time delay neural network (TDNN) for speech enhancement with full data learning. To make full use of the training data, we propose a full data learning method for speech enhancement.
  arXiv  Detail & Related papers  (2020-11-11T06:32:37Z)
• Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey [77.99182201815763]
  Deep Neural Networks (DNNs) achieve state-of-the-art results in many different problem settings. DNNs are often treated as black box systems, which complicates their evaluation and validation. One promising field, inspired by the success of convolutional neural networks (CNNs) in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations.
  arXiv  Detail & Related papers  (2020-06-30T14:56:05Z)

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.