COMPUTER VISION
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors
August 18, 2020
Abstract
We present a systematic study of the transferability of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, we train patterns that suppress the objectness scores produced by a range of commonly used detectors, and ensembles of detectors. Through extensive experiments, we benchmark the effectiveness of adversarially trained patches under both white-box and black-box settings, and quantify transferability of attacks between datasets, object classes, and detector models. Finally, we present a de- tailed study of physical world attacks using printed posters and wearable clothes, and rigorously quantify the performance of such attacks with different metrics.
AUTHORS
Written by
Ser-Nam Lim
Larry Davis
Tom Goldstein
Zuxuan Wu
Publisher
ECCV
Research Topics
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