English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, we study physical adversarial attacks on object detectors inthe wild. Previous works mostly craft instance-dependent perturbations only forrigid or planar objects. To this end, we propose to learn an adversarialpattern to effectively attack all instances belonging to the same objectcategory, referred to as Universal Physical Camouflage Attack (UPC).Concretely, UPC crafts camouflage by jointly fooling the region proposalnetwork, as well as misleading the classifier and the regressor to outputerrors. In order to make UPC effective for non-rigid or non-planar objects, weintroduce a set of transformations for mimicking deformable properties. Weadditionally impose optimization constraint to make generated patterns looknatural to human observers. To fairly evaluate the effectiveness of differentphysical-world attacks, we present the first standardized virtual database,AttackScenes, which simulates the real 3D world in a controllable andreproducible environment. Extensive experiments suggest the superiority of ourproposed UPC compared with existing physical adversarial attackers not only invirtual environments (AttackScenes), but also in real-world physicalenvironments. Code and dataset are available athttps://mesunhlf.github.io/index_physical.html.

Universal Physical Camouflage Attacks on Object Detectors