On Physical Adversarial Patches for Object Detection

06/20/2019
by   Mark Lee, et al.
0

In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can suppress virtually all the detected objects in the image. That is, we can place the patch anywhere in the image, causing all existing objects in the image to be missed entirely by the detector, even those far away from the patch itself. This in turn opens up new lines of physical attacks against object detection systems, which require no modification of the objects in a scene. A demo of the system can be found at https://youtu.be/WXnQjbZ1e7Y.

READ FULL TEXT
research
12/23/2020

The Translucent Patch: A Physical and Universal Attack on Object Detectors

Physical adversarial attacks against object detectors have seen increasi...
research
10/16/2020

DPAttack: Diffused Patch Attacks against Universal Object Detection

Recently, deep neural networks (DNNs) have been widely and successfully ...
research
02/05/2021

DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks

State-of-the-art object detectors are vulnerable to localized patch hidi...
research
09/30/2021

You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors

Blind spots or outright deceit can bedevil and deceive machine learning ...
research
06/14/2023

X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail

Object detection models, which are widely used in various domains (such ...
research
06/09/2021

We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature

Recently, the object detection based on deep learning has proven to be v...
research
09/30/2019

Adversarial Patches Exploiting Contextual Reasoning in Object Detection

The usefulness of spatial context in most fast object detection algorith...

Please sign up or login with your details

Forgot password? Click here to reset