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Computer Science > Computer Vision and Pattern Recognition

arXiv:1910.14667 (cs)
[Submitted on 31 Oct 2019 (v1), last revised 22 Jul 2020 (this
version, v2)]

Title:Making an Invisibility Cloak: Real World Adversarial Attacks on
Object Detectors

Authors:Zuxuan Wu, Ser-Nam Lim, Larry Davis, Tom Goldstein
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    Abstract: We present a systematic study 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 detailed study of physical
    world attacks using printed posters and wearable clothes, and
    rigorously quantify the performance of such attacks with
    different metrics.

Comments: ECCV 2020
          Computer Vision and Pattern Recognition (cs.CV);
Subjects: Cryptography and Security (cs.CR); Machine Learning
          (cs.LG); Optimization and Control (math.OC)
Cite as:  arXiv:1910.14667 [cs.CV]
          (or arXiv:1910.14667v2 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.1910.14667
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Zuxuan Wu [view email]
[v1] Thu, 31 Oct 2019 17:56:29 UTC (8,352 KB)
[v2] Wed, 22 Jul 2020 17:59:49 UTC (6,092 KB)
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Larry Davis
Tom Goldstein
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