mariusblarsen / ae-attack-pipeline

Pipeline for executing adversarial example input attacks against DNNs. Developed as a part of Specialization project at NTNU

Pipeline for adversarial input attacks against obstacle detection

Repository for the Specialization project at NTNU fall 2021.

Authors

• Johannes Åsheim
• Kim Midtlid
• Marius Bæver Larsen

Todo

• Possibility to change/add models
• Possibility to change/add attacks
• Option for GPU acceleration (When training is needed)
• Add defence step

Setup

1. Create and activate new environment

python -m venv project

Linux:

source project/Scripts/activate

Windows:

.\project\Scripts\activate 

2. Install ipykernel and add virtual environment to the Python Kernel

pip install ipykernel

python -m ipykernel install --user --name=project

ae-attack-pipeline's Issues

Welcome update to OpenMMLab 2.0

Welcome update to OpenMMLab 2.0

I am Vansin, the technical operator of OpenMMLab. In September of last year, we announced the release of OpenMMLab 2.0 at the World Artificial Intelligence Conference in Shanghai. We invite you to upgrade your algorithm library to OpenMMLab 2.0 using MMEngine, which can be used for both research and commercial purposes. If you have any questions, please feel free to join us on the OpenMMLab Discord at https://discord.gg/amFNsyUBvm or add me on WeChat (van-sin) and I will invite you to the OpenMMLab WeChat group.

Here are the OpenMMLab 2.0 repos branches:

	OpenMMLab 1.0 branch	OpenMMLab 2.0 branch
MMEngine		0.x
MMCV	1.x	2.x
MMDetection	0.x 、1.x、2.x	3.x
MMAction2	0.x	1.x
MMClassification	0.x	1.x
MMSegmentation	0.x	1.x
MMDetection3D	0.x	1.x
MMEditing	0.x	1.x
MMPose	0.x	1.x
MMDeploy	0.x	1.x
MMTracking	0.x	1.x
MMOCR	0.x	1.x
MMRazor	0.x	1.x
MMSelfSup	0.x	1.x
MMRotate	1.x	1.x
MMYOLO		0.x

Attention: please create a new virtual environment for OpenMMLab 2.0.