TY - GEN
T1 - An Universal Perturbation Generator for Black-Box Attacks Against Object Detectors
AU - Zhao, Yuhang
AU - Wang, Kunqing
AU - Xue, Yuan
AU - Zhang, Quanxin
AU - Zhang, Xiaosong
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - With the continuous development of deep neural networks (DNNs), it has become the main means of solving problems in the field of computer vision. However, recent research has shown that deep neural networks are vulnerable to well-designed adversarial examples. In this paper, we used a deep neural network to generate adversarial examples to attack black-box object detectors. We trained a generation network to produce universal perturbations, achieving a cross-task attack against black-box object detectors. We demonstrated the feasibility of task-generalizable attacks. Our attack generated efficient universal perturbations on classifiers then attack object detectors. We proved the effectiveness of our attack on two representative object detectors: Faster R-CNN based on proposal and regression-based YOLOv3.
AB - With the continuous development of deep neural networks (DNNs), it has become the main means of solving problems in the field of computer vision. However, recent research has shown that deep neural networks are vulnerable to well-designed adversarial examples. In this paper, we used a deep neural network to generate adversarial examples to attack black-box object detectors. We trained a generation network to produce universal perturbations, achieving a cross-task attack against black-box object detectors. We demonstrated the feasibility of task-generalizable attacks. Our attack generated efficient universal perturbations on classifiers then attack object detectors. We proved the effectiveness of our attack on two representative object detectors: Faster R-CNN based on proposal and regression-based YOLOv3.
KW - Adversarial attack
KW - Adversarial example
KW - Deep learning
UR - http://www.scopus.com/inward/record.url?scp=85076129873&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-34139-8_7
DO - 10.1007/978-3-030-34139-8_7
M3 - Conference contribution
AN - SCOPUS:85076129873
SN - 9783030341381
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 63
EP - 72
BT - Smart Computing and Communication - 4th International Conference, SmartCom 2019, Proceedings
A2 - Qiu, Meikang
PB - Springer
T2 - 4th International Conference on Smart Computing and Communications, SmartCom 2019
Y2 - 11 October 2019 through 13 October 2019
ER -