This repository contains the implementation of the paper: "Towards Frequency-Based Explanation for Robust CNN"

Related tags

Deep Learningpythonmachine-learningcnnrobustness
Overview

RobustFreqCNN

About

This repository contains the implementation of the paper "Towards Frequency-Based Explanation for Robust CNN" arxiv. It primarly deals the extent to which image features are robust in the frequency domain.

Steps

  1. It is recommended to setup a fresh virtual environment first.
python -m venv env
source activate env/bin/activate
  1. Install the torchattacks package
pip install torchattacks
  1. Run the main.py file.

The original paper implemented the attacks using a VGG 19 model. However, due to memory constraints I did it using ResNet 18. Here I have provided a fine-tuned version of ResNet 18 which is pre-trained on ImageNet. The checkpoint can be downloaded using this link.

RCT Maps

CW FGSM PGD

Disclaimer

This is not an official implementation of the paper. I am not associated with the authors of the paper or Lab in any manner whatsoever and I don't claim credit for any of the algorithms proposed in the paper.

Owner
Sarosij Bose
Sophomore at University of Calcutta, Technology Campus. 3D Vision || Deep Learning.
Sarosij Bose
GitHub Repository https://arxiv.org/pdf/2005.03141.pdf
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