3D Invisible Cloak: A Robust Person Stealth Attack Against Object Detector in Complex 3D Physical Scenarios

  • Mingfu Xue*
  • , Can He
  • , Yushu Zhang
  • , Zhe Liu
  • , Weiqiang Liu
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this article, we propose a novel physical stealth attack against the person detectors in real world. For the first time, we consider the impacts of those complex and challenging 3D physical constraints (e.g., radian, wrinkle, occlusion, angle, etc.) on person stealth attacks, and propose 3D transformations to generate robust 3D invisible cloak. We launch the person stealth attacks in 3D physical space instead of 2D plane by printing the adversarial patches on real clothes. Anyone wearing the cloak can evade the detection of person detectors and achieve stealth under challenging and complex 3D physical scenarios. Experimental results in various indoor and outdoor physical scenarios show that, the proposed person stealth attack method is robust and effective even under those complex and challenging physical conditions, such as the cloak is wrinkled, obscured, curved, and from different/large angles. The attack success rate of the generated adversarial patch in digital domain (Inria dataset) is 86.56% against YOLO v2 and 80.32% against YOLO v5, while the static and dynamic stealth attack success rates of the generated 3D invisible cloak in physical world are 100%, 77% against YOLO v2 and 100%, 83.95% against YOLO v5, respectively, which are significantly better than state-of-the-art works.

Original languageEnglish
Pages (from-to)799-815
Number of pages17
JournalIEEE Transactions on Emerging Topics in Computing
Volume13
Issue number3
DOIs
StatePublished - 2025

Keywords

  • Artificial intelligence security
  • adversarial examples
  • deep learning
  • object detector
  • physical adversarial attack

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