A repository for the generation, visualization, and evaluation of patch based adversarial attacks on the yoloV3 object detection system
This repository contains python code to generate, evaluate, and visualize a patch based adversarial example for the Yolov3 object detector, a copy of which is included here. Some of the code in this repository has been pulled directly from other repositories, as cited in my research report.
This is research code, funded by a 8 week Summer Research Fellowship Grant (SOAR-NSE) from Swarthmore College.
The associated paper can be found at https://arxiv.org/abs/2008.10106
This code supports multi-threading and is both cpu and gpu compatable. In config.gin specify ‘cpu’ to run on a cpu, ‘cuda’ to run on any available gpu or ‘cuda:[gpu number]’ to run on a specific gpu.
The code runs on macOS and linux.
Python 3.7 or later with all requirements.txt
dependencies installed. To install all dependencies run:
$ pip install -U -r requirements.txt
$ python adversarial_attack.py config.gin
$ python adversarial_attack.py config.gin
$ python adversarial_attack.py config.gin
$ python adversarial_attack.py config.gin
Got questions? Email me at ianmcdiarmidsterling at gmail dot com
Special thank you for discussion, guidance, and support to:
This repository will not be updated after 8/1/2020
Thank you to all grant donors at Swarthmore College who make undergraduate research possible.