Layered Defense Net

This repository is the Python implementation of the "DefenseLayer", an intra-model defense layer approach to securing deep learning image classifiers against adversarial attacks. The paper describing our approach, titled "Defending Against Adversarial Attacks One Layer at a Time", is included in this repository.

Project presentation can be found: here

Provided Code Files:

GetImagenet.ipynb

• Extracts an ImageNet test dataset composed of two classes: bikes and ships.

FoolBoxOnImageNet.ipynb

• Creates FGSM and DeepFool attacks based on the ImageNet dataset.

Testing.ipynb

• Inserts the wavelet denoising layers into the model
• Tests the modifed models on the various test datasets

Graphing.ipynb

• Generates the graphs included in the paper

Contributers:

• jkurian49 - (https://github.com/jkurian49)