Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones

• URL: http://arxiv.org/abs/2307.01559v1
• Date: Tue, 4 Jul 2023 08:29:41 GMT
• Title: Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones
• Authors: Elia Cereda and Alessandro Giusti and Daniele Palossi
• Abstract summary: Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communication link, can not be trusted. We propose a novel distributed edge-fog execution scheme that validates fog computation by redundantly executing a random subnetwork aboard our nano-drone.
• Score: 75.80952211739185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communication link, can not be trusted. To tackle this concern, we propose a novel distributed edge-fog execution scheme that validates fog computation by redundantly executing a random subnetwork aboard our nano-drone. Compared to a State-of-the-Art visual pose estimation network that entirely runs onboard, a larger network executed in a distributed way improves the $R^2$ score by +0.19; in case of attack, our approach detects it within 2s with 95% probability.

Related papers

• Edge-Only Universal Adversarial Attacks in Distributed Learning [49.546479320670464]
  In this work, we explore the feasibility of generating universal adversarial attacks when an attacker has access to the edge part of the model only. Our approach shows that adversaries can induce effective mispredictions in the unknown cloud part by leveraging key features on the edge side. Our results on ImageNet demonstrate strong attack transferability to the unknown cloud part.
  arXiv  Detail & Related papers  (2024-11-15T11:06:24Z)
• A Fast Algorithm for Moderating Critical Nodes via Edge Removal [19.130541561303293]
  We study the problem of removing $k$ edges from a network to minimize the information centrality of a target node. We propose three approximation greedy algorithms using novel techniques such as random walk-based Schur complement approximation and fast sum estimation. To complement our theoretical analysis, we conduct a comprehensive set of experiments on synthetic and real networks with over one million nodes.
  arXiv  Detail & Related papers  (2023-09-09T13:54:34Z)
• Wasserstein distributional robustness of neural networks [9.79503506460041]
  Deep neural networks are known to be vulnerable to adversarial attacks (AA) For an image recognition task, this means that a small perturbation of the original can result in the image being misclassified. We re-cast the problem using techniques of Wasserstein distributionally robust optimization (DRO) and obtain novel contributions.
  arXiv  Detail & Related papers  (2023-06-16T13:41:24Z)
• Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach [64.23331120621118]
  This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks. We introduce a related embedded network and show that the embedded network can be used to provide an $ell_infty$-norm box over-approximation of the reachable sets of the original network. We apply our algorithms to train implicit neural networks on the MNIST dataset and compare the robustness of our models with the models trained via existing approaches in the literature.
  arXiv  Detail & Related papers  (2022-08-08T03:13:24Z)
• Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm [93.80082636284922]
  Sparse adversarial attacks can fool deep networks (DNNs) by only perturbing a few pixels. Recent efforts combine it with another l_infty perturbation on magnitudes. We propose a homotopy algorithm to tackle the sparsity and neural perturbation framework.
  arXiv  Detail & Related papers  (2021-06-10T20:11:36Z)
• Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits [55.740716446995805]
  We study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Our goal is to misclassify a specific sample into a target class without any sample modification. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem.
  arXiv  Detail & Related papers  (2021-02-21T03:13:27Z)
• Against Membership Inference Attack: Pruning is All You Need [22.13027338667513]
  We envision that the weight pruning technique will help deep learning networks (DNNs) against membership inference attack (MIA) In this work, we propose a pruning algorithm, and we show that the proposed algorithm can find a subnetwork that can prevent privacy leakage from MIA. Our experimental results illustrate that the attack accuracy using model compression is up to 13.6% and 10% lower than that of the baseline and Min-Max game, accordingly.
  arXiv  Detail & Related papers  (2020-08-28T02:15:44Z)
• ESPN: Extremely Sparse Pruned Networks [50.436905934791035]
  We show that a simple iterative mask discovery method can achieve state-of-the-art compression of very deep networks. Our algorithm represents a hybrid approach between single shot network pruning methods and Lottery-Ticket type approaches.
  arXiv  Detail & Related papers  (2020-06-28T23:09:27Z)
• Depth-2 Neural Networks Under a Data-Poisoning Attack [2.105564340986074]
  We study the possibility of defending against data-poisoning attacks while training a shallow neural network in a regression setup. In this work, we focus on doing supervised learning for a class of depth-2 finite-width neural networks.
  arXiv  Detail & Related papers  (2020-05-04T17:56:15Z)

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.