Dynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural Networks

• URL: http://arxiv.org/abs/2308.08709v1
• Date: Thu, 17 Aug 2023 00:15:11 GMT
• Title: Dynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural Networks
• Authors: Mirazul Haque and Wei Yang
• Abstract summary: We investigate the robustness of dynamic mechanism in DyNNs and how dynamic mechanism design impacts the robustness of DyNNs. We find that attack transferability from DyNNs to SDNNs is higher than attack transferability from SDNNs to DyNNs. Also, we find that DyNNs can be used to generate adversarial samples more efficiently than SDNNs.
• Score: 10.225238909616104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Neural Networks (DNNs) have been used to solve different day-to-day problems. Recently, DNNs have been deployed in real-time systems, and lowering the energy consumption and response time has become the need of the hour. To address this scenario, researchers have proposed incorporating dynamic mechanism to static DNNs (SDNN) to create Dynamic Neural Networks (DyNNs) performing dynamic amounts of computation based on the input complexity. Although incorporating dynamic mechanism into SDNNs would be preferable in real-time systems, it also becomes important to evaluate how the introduction of dynamic mechanism impacts the robustness of the models. However, there has not been a significant number of works focusing on the robustness trade-off between SDNNs and DyNNs. To address this issue, we propose to investigate the robustness of dynamic mechanism in DyNNs and how dynamic mechanism design impacts the robustness of DyNNs. For that purpose, we evaluate three research questions. These evaluations are performed on three models and two datasets. Through the studies, we find that attack transferability from DyNNs to SDNNs is higher than attack transferability from SDNNs to DyNNs. Also, we find that DyNNs can be used to generate adversarial samples more efficiently than SDNNs. Then, through research studies, we provide insight into the design choices that can increase robustness of DyNNs against the attack generated using static model. Finally, we propose a novel attack to understand the additional attack surface introduced by the dynamic mechanism and provide design choices to improve robustness against the attack.

Related papers

• Robust Stable Spiking Neural Networks [45.84535743722043]
  Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low energy budget on neuromorphic hardware. Many studies have been conducted to defend SNNs from the threat of adversarial attacks. This paper aims to uncover the robustness of SNN through the lens of the stability of nonlinear systems.
  arXiv  Detail & Related papers  (2024-05-31T08:40:02Z)
• Inherent Redundancy in Spiking Neural Networks [24.114844269113746]
  Spiking Networks (SNNs) are a promising energy-efficient alternative to conventional artificial neural networks. In this work, we focus on three key questions regarding inherent redundancy in SNNs. We propose an Advance Attention (ASA) module to harness SNNs' redundancy.
  arXiv  Detail & Related papers  (2023-08-16T08:58:25Z)
• Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility [0.0]
  Physics-Informed Neural Networks (PINNs) have emerged as a promising solution for drastically accelerating computations of non-linear dynamical systems. This work investigates the applicability of these methods for power system dynamics, focusing on the dynamic response to load disturbances. To facilitate a deeper understanding, this paper also present a new regularisation of Neural Network (NN) training by introducing a gradient-based term in the loss function.
  arXiv  Detail & Related papers  (2023-03-15T23:53:32Z)
• 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)
• On the Intrinsic Structures of Spiking Neural Networks [66.57589494713515]
  Recent years have emerged a surge of interest in SNNs owing to their remarkable potential to handle time-dependent and event-driven data. There has been a dearth of comprehensive studies examining the impact of intrinsic structures within spiking computations. This work delves deep into the intrinsic structures of SNNs, by elucidating their influence on the expressivity of SNNs.
  arXiv  Detail & Related papers  (2022-06-21T09:42:30Z)
• Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead [67.87678914831477]
  Deep Neural Networks (DNNs) enable a wide series of technological advancements. Recent findings indicate that transient hardware faults may corrupt the models prediction dramatically. In this work, we propose to tackle the reliability issue both at training and model design time.
  arXiv  Detail & Related papers  (2022-05-28T13:09:30Z)
• 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)
• Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks [98.21130211336964]
  Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. In this paper, we investigate the impact of network width and depth on the robustness of adversarially trained DNNs.
  arXiv  Detail & Related papers  (2021-10-07T23:13:33Z)
• R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors [15.093607722961407]
  Spiking Neural Networks (SNNs) aim at providing energy-efficient learning capabilities when implemented on neuromorphic chips with event-based Dynamic Vision Sensors (DVS) This paper studies the robustness of SNNs against adversarial attacks on such DVS-based systems, and proposes R-SNN, a novel methodology for robustifying SNNs through efficient noise filtering.
  arXiv  Detail & Related papers  (2021-09-01T14:40:04Z)
• On the benefits of robust models in modulation recognition [53.391095789289736]
  Deep Neural Networks (DNNs) using convolutional layers are state-of-the-art in many tasks in communications. In other domains, like image classification, DNNs have been shown to be vulnerable to adversarial perturbations. We propose a novel framework to test the robustness of current state-of-the-art models.
  arXiv  Detail & Related papers  (2021-03-27T19:58:06Z)

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.