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

Recently, deep neural networks (DNNs) have been widely and successfully usedin Object Detection, e.g. Faster RCNN, YOLO, CenterNet. However, recent studieshave shown that DNNs are vulnerable to adversarial attacks. Adversarial attacksagainst object detection can be divided into two categories, whole-pixelattacks and patch attacks. While these attacks add perturbations to a largenumber of pixels in images, we proposed a diffused patch attack(\textbf{DPAttack}) to successfully fool object detectors by diffused patchesof asteroid-shaped or grid-shape, which only change a small number of pixels.Experiments show that our DPAttack can successfully fool most object detectorswith diffused patches and we get the second place in the Alibaba Tianchicompetition: Alibaba-Tsinghua Adversarial Challenge on Object Detection. Ourcode can be obtained from https://github.com/Wu-Shudeng/DPAttack.

DPAttack: Diffused Patch Attacks against Universal Object Detection