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A small course on exploiting and defending neural networks
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Kayzaks Updated Solution for Exercise 3
- The solution had a bug in it that made it report the wrong success rate. Fixed and modified it to work again, making the exercise slightly harder.
- Adjusted the Exercise to include an easy version (the old one, which only works up to 5%) and a slightly harder one (the new 10%)
Latest commit 70f81c5 Nov 26, 2019

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0_LastLayerAttack Instructions for downloading scikit-image Nov 22, 2019
1_Backdooring Instructions for downloading scikit-image Nov 22, 2019
2_ExtractingInformation Instructions for downloading scikit-image Nov 22, 2019
3_BruteForcing Updated Solution for Exercise 3 Nov 26, 2019
4_NeuralOverflow Replace Scipy by Skimage Nov 21, 2019
5_MalwareInjection Merge pull request #3 from PatrickSpeicher/patch-2 Nov 26, 2019
6_NeuralObfuscation Replace Scipy by Skimage Nov 21, 2019
7_BugHunter Replace Scipy by Skimage Nov 21, 2019
8_GPUAttack Instructions for downloading scikit-image Nov 22, 2019
.gitattributes Initial commit Oct 15, 2019
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Article.pdf Instructions for downloading scikit-image Nov 22, 2019
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README.md Instructions for downloading scikit-image Nov 22, 2019

README.md

Hacking Neural Networks: A Short Introduction

Disclaimer: This article and all the associated exercises are for educational purposes only.

This is a short introduction on methods that use neural networks in an offensive manner (bug hunting, shellcode obfuscation, etc.) and how to exploit neural networks found in the wild (information extraction, malware injection, backdooring, etc.).

Most of the methods presented are accompanied by an exercise found in this repo. The full article can be found here in 'Article.pdf' or on arXiv (arXiv:1911.07658).


Setup

Python and pip

Download and install Python3 and its package installer pip using a package manager or directly from the website https://www.python.org/downloads/.

Editor

An editor is required to work with the code, preferably one that allows code highlighting for Python. Vim/Emacs will do. As a reference, all exercises were prepared using Visual Studio Code https://code.visualstudio.com/docs/python/python-tutorial.

Packages


The exercises

  • 0 - Last Layer Attack
  • 1 - Backdooring
  • 2 - Extracting Information
  • 3 - Brute Forcing
  • 4 - Neural Overflow
  • 5 - Malware Injection
  • 6 - Neural Obfuscation
  • 7 - Bug Hunting
  • 8 - GPU Attack

For instructions, please read the 'README.md' file in each of the exercise directories.


Further Reading / Watching

Check out:


What else?

The neural networks found in the exercises are based on the examples provided by keras.

If you find that there are errors or missing references, feel free to make a PR or contact me.

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