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.

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AUTHORS

Written by

Ser-Nam Lim

Larry Davis

Tom Goldstein

Zuxuan Wu

Publisher

ECCV

Research Topics

Computer Vision

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