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Eroding Trust In Aerial Imagery: Comprehensive Analysis and Evaluation Of Adversarial Attacks In Geospatial Systems

arXiv 2312.07389 ↗First posted 12 Dec 2023cs.CVcited by 0

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Abstract (arXiv)

In critical operations where aerial imagery plays an essential role, the integrity and trustworthiness of data are paramount. The emergence of adversarial attacks, particularly those that exploit control over labels or employ physically feasible trojans, threatens to erode that trust, making the analysis and mitigation of these attacks a matter of urgency. We demonstrate how adversarial attacks can degrade confidence in geospatial systems, specifically focusing on scenarios where the attacker's control over labels is restricted and the use of realistic threat vectors. Proposing and evaluating several innovative attack methodologies, including those tailored to overhead images, we empirically show their threat to remote sensing systems using high-quality SpaceNet datasets. Our experimentation reflects the unique challenges posed by aerial imagery, and these preliminary results not only reveal the potential risks but also highlight the non-trivial nature of the problem compared to recent works.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 12 Dec 2023.

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References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  5. 2022Generative Adversarial Networkscited by 5,627
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  8. 2020Invisible Backdoor Attack with Sample-Specific Triggerscited by 353
  9. 2019Fooling automated surveillance cameras: adversarial patches to attack person detectioncited by 268
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  11. 2019On Physical Adversarial Patches for Object Detectioncited by 93
  12. 2022BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learningcited by 86
10 more
  1. 2020Rethinking the Trigger of Backdoor Attackcited by 77
  2. 2019Deep Image Blendingcited by 62
  3. 2018AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learningcited by 60
  4. 2022Are Attribute Inference Attacks Just Imputation?cited by 49
  5. 2021DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injectioncited by 44
  6. 2021Poisoning the Unlabeled Dataset of Semi-Supervised Learningcited by 44
  7. 2021Physical Adversarial Attacks on an Aerial Imagery Object Detectorcited by 35
  8. 2020Adversarial Patch Camouflage against Aerial Detectioncited by 16
  9. 2022Privacy-preserving Generative Framework Against Membership Inference Attackscited by 3
  10. 2022The Weaknesses of Adversarial Camouflage in Overhead Imagerycited by 2

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