Shaped Adversarial Patches and the Ability They Hold

In more recent years the development of computer vision has advanced to be more comprehensive than in the past, with newer applications ranging from autonomous vehicles to security systems. The main application I will be talking about throughout this paper is an object detection algorithm called YOLO(You only look once), this algorithm is particularly significant due to their real-time performance of being able to identify and localize objects within an image in quick timing. However, the strength of these computer vision models is increasingly challenged by adversarial attacks, which manipulate the computer's vision to block a certain part of the image out or to confuse the software into thinking it is something else. Throughout this research paper I will be explaining how YOLO works and how shaped adversarial patches are able to fool this algorithm.

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Publication Details

Journal
ODU Digital Commons (Old Dominion University)
Published
2025-05-01
DOI
https://doi.org/10.25776/vs1t-y532
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
0.00
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article

Shaped Adversarial Patches and the Ability They Hold

ODU Digital Commons (Old Dominion University)
Species Distribution and Climate Change
article

Shaped Adversarial Patches and the Ability They Hold

article en

Abstract

In more recent years the development of computer vision has advanced to be more comprehensive than in the past, with newer applications ranging from autonomous vehicles to security systems. The main application I will be talking about throughout this paper is an object detection algorithm called YOLO(You only look once), this algorithm is particularly significant due to their real-time performance of being able to identify and localize objects within an image in quick timing. However, the strength of these computer vision models is increasingly challenged by adversarial attacks, which manipulate the computer's vision to block a certain part of the image out or to confuse the software into thinking it is something else. Throughout this research paper I will be explaining how YOLO works and how shaped adversarial patches are able to fool this algorithm.

ODU Digital Commons (Old Dominion University)
Dominion University College (GH), Old Dominion University (US)
Openalex Percentile: Top 62%
Species Distribution and Climate Change
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