TY - JOUR
T1 - 3D Invisible Cloak
T2 - A Robust Person Stealth Attack Against Object Detector in Complex 3D Physical Scenarios
AU - Xue, Mingfu
AU - He, Can
AU - Zhang, Yushu
AU - Liu, Zhe
AU - Liu, Weiqiang
N1 - Publisher Copyright:
© IEEE. 2013 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Artificial intelligence security
KW - adversarial examples
KW - deep learning
KW - object detector
KW - physical adversarial attack
UR - https://www.scopus.com/pages/publications/85212347006
U2 - 10.1109/TETC.2024.3513392
DO - 10.1109/TETC.2024.3513392
M3 - 文章
AN - SCOPUS:85212347006
SN - 2168-6750
VL - 13
SP - 799
EP - 815
JO - IEEE Transactions on Emerging Topics in Computing
JF - IEEE Transactions on Emerging Topics in Computing
IS - 3
ER -