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Detection of Adversarial Attacks in Robotic Perception

arXiv 2603.28594 ↗First posted 30 Mar 2026cs.CV cs.AI cs.CRcited by 0

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Abstract (arXiv)

Deep Neural Networks (DNNs) achieve strong performance in semantic segmentation for robotic perception but remain vulnerable to adversarial attacks, threatening safety-critical applications. While robustness has been studied for image classification, semantic segmentation in robotic contexts requires specialized architectures and detection strategies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 30 Mar 2026.

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References · 18

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6 more
  1. 2017On the (Statistical) Detection of Adversarial Examplescited by 180
  2. 2018Understanding Measures of Uncertainty for Adversarial Example Detectioncited by 177
  3. 2014Hypercolumns for Object Segmentation and Fine-grained Localizationcited by 128
  4. 2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 66
  5. 2017When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Timecited by 9
  6. 2024Detecting Adversarial Examplescited by 2

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