CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization

• URL: http://arxiv.org/abs/2311.06361v1
• Date: Fri, 10 Nov 2023 19:26:31 GMT
• Title: CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization
• Authors: Danish Gufran, Sudeep Pasricha
• Abstract summary: We introduce CALLOC, a novel framework designed to resist adversarial attacks and variations across indoor environments and devices. CALLOC employs a novel adaptive curriculum learning approach with a domain specific lightweight scaled-dot product attention neural network. We show that CALLOC can achieve improvements of up to 6.03x in mean error and 4.6x in worst-case error against state-of-the-art indoor localization frameworks.
• Score: 3.943289808718775
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Indoor localization has become increasingly vital for many applications from tracking assets to delivering personalized services. Yet, achieving pinpoint accuracy remains a challenge due to variations across indoor environments and devices used to assist with localization. Another emerging challenge is adversarial attacks on indoor localization systems that not only threaten service integrity but also reduce localization accuracy. To combat these challenges, we introduce CALLOC, a novel framework designed to resist adversarial attacks and variations across indoor environments and devices that reduce system accuracy and reliability. CALLOC employs a novel adaptive curriculum learning approach with a domain specific lightweight scaled-dot product attention neural network, tailored for adversarial and variation resilience in practical use cases with resource constrained mobile devices. Experimental evaluations demonstrate that CALLOC can achieve improvements of up to 6.03x in mean error and 4.6x in worst-case error against state-of-the-art indoor localization frameworks, across diverse building floorplans, mobile devices, and adversarial attacks scenarios.

Related papers

• SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks [2.7186493234782527]
  We present SENTINEL, a novel embedded machine learning framework to bolster the resilience of indoor localization solutions against adversarial attacks. We also introduce RSSRogueLoc, a dataset capturing the effects of rogue APs from several real-world indoor environments.
  arXiv  Detail & Related papers  (2024-07-14T21:40:12Z)
• DiffuBox: Refining 3D Object Detection with Point Diffusion [74.01759893280774]
  We introduce a novel diffusion-based box refinement approach to ensure robust 3D object detection and localization. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets.
  arXiv  Detail & Related papers  (2024-05-25T03:14:55Z)
• STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization [2.9699290794642366]
  Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. We propose STELLAR, a novel framework implementing a contrastive learning approach. We show 8-75% improvements in accuracy compared to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2023-12-16T04:12:36Z)
• ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models [65.79770974145983]
  ASSERT, Automated Safety Scenario Red Teaming, consists of three methods -- semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection. We partition our prompts into four safety domains for a fine-grained analysis of how the domain affects model performance. We find statistically significant performance differences of up to 11% in absolute classification accuracy among semantically related scenarios and error rates of up to 19% absolute error in zero-shot adversarial settings.
  arXiv  Detail & Related papers  (2023-10-14T17:10:28Z)
• FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices [4.226118870861363]
  Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. We propose a novel embedded machine learning framework called FedHIL to improve indoor localization accuracy in device-heterogeneous environments. Our framework combines indoor localization and federated learning (FL) to improve indoor localization accuracy in device-heterogeneous environments.
  arXiv  Detail & Related papers  (2023-07-04T15:34:13Z)
• FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations [52.85536740465277]
  FIRE is a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. We show that FIRE reduces costs compared to vanilla RL and the greedy baseline in the event of failures.
  arXiv  Detail & Related papers  (2022-09-28T19:49:39Z)
• Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization [3.577310844634503]
  Smartphones together with RSSI fingerprinting serve as an efficient approach for delivering a low-cost and high-accuracy indoor localization solution. We propose a multi-head attention neural network-based indoor localization framework that is resilient to device heterogeneity. An in-depth analysis of our proposed framework demonstrates up to 35% accuracy improvement compared to state-of-the-art indoor localization techniques.
  arXiv  Detail & Related papers  (2022-05-17T03:08:09Z)
• Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices [5.063728016437489]
  Fingerprinting-based indoor localization is an emerging application domain for enhanced positioning and tracking of people and assets within indoor locales. We propose a Siamese neural encoder-based framework that offers up to 40% reduction in degradation of localization accuracy over time compared to the state-of-the-art in the area.
  arXiv  Detail & Related papers  (2021-11-28T07:22:55Z)
• Attribute-Guided Adversarial Training for Robustness to Natural Perturbations [64.35805267250682]
  We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations.
  arXiv  Detail & Related papers  (2020-12-03T10:17:30Z)
• Domain Adaptation of Learned Features for Visual Localization [60.6817896667435]
  We tackle the problem of visual localization under changing conditions, such as time of day, weather, and seasons. Recent learned local features based on deep neural networks have shown superior performance over classical hand-crafted local features. We present a novel and practical approach, where only a few examples are needed to reduce the domain gap.
  arXiv  Detail & Related papers  (2020-08-21T05:17:32Z)
• Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis [76.46004354572956]
  We introduce an unsupervised domain adaptation approach for person re-identification. Experimental results show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance.
  arXiv  Detail & Related papers  (2020-01-14T17:43:52Z)

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.