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https://github.com/man3kin3ko/awesome-adversarial-machine-learning

A curated list of awesome AML attacks and defences frameworks and resources.
https://github.com/man3kin3ko/awesome-adversarial-machine-learning

List: awesome-adversarial-machine-learning

adversarial-attacks adversarial-machine-learning aisec awesome-list blue-team ctf cybersecurity mlsec red-team

Last synced: about 1 month ago
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A curated list of awesome AML attacks and defences frameworks and resources.

Host: GitHub
URL: https://github.com/man3kin3ko/awesome-adversarial-machine-learning
Owner: man3kin3ko
License: cc0-1.0
Created: 2022-04-12T12:22:57.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2022-08-29T15:54:44.000Z (about 2 years ago)
Last Synced: 2024-05-23T00:08:51.570Z (4 months ago)
Topics: adversarial-attacks, adversarial-machine-learning, aisec, awesome-list, blue-team, ctf, cybersecurity, mlsec, red-team
Homepage:
Size: 149 KB
Stars: 4
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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ultimate-awesome - awesome-adversarial-machine-learning - A curated list of awesome AML attacks and defences frameworks and resources. (Other Lists / PowerShell Lists)

README

        # Awesome Adversarial Machine Learning [![Awesome](https://awesome.re/badge-flat.svg)](https://awesome.re)

A curated list of awesome Machine Learning Security resources.

Also see [awesome-ml-for-cybersecurity](https://github.com/jivoi/awesome-ml-for-cybersecurity) and [The Definitive Security Data Science and Machine Learning Guide](http://www.covert.io/the-definitive-security-datascience-and-machinelearning-guide/).

- [Awesome Adversarial Machine Learning](#awesome-adversarial-machine-learning-)

  - [Terminology](#terminology)

  - [Threat Modeling](#threat-modeling)

  - [Controls Guidelines](#controls-guidelines)

  - [Case Studies](#case-studies)

  - [Attacks based on domain](#attacks-based-on-domain)

  - [Attacks based on strategy](#attacks-based-on-strategy)

  - [CTF and Hackathons](#ctf-and-hackathons)

  - [Frameworks](#frameworks)

## Terminology

* [NIST: A Taxonomy and Terminology of Adversarial Machine Learning](https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8269-draft.pdf)

## Threat Modeling

* [ENISA: Artificial Intelligence Cybersecurity Challenges](https://www.enisa.europa.eu/news/publications/artificial-intelligence-cybersecurity-challenges)

* [MITRE: Adversarial Threat Landscape for Artificial-Intelligence Systems](https://atlas.mitre.org/)

* [The Threat of Offensive AI to Organizations](https://arxiv.org/pdf/2106.15764.pdf)

* [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://arxiv.org/abs/1801.00553)

## Controls Guidelines

* [ENISA: Securing Machine Learning Algorithms](https://www.enisa.europa.eu/publications/securing-machine-learning-algorithms)

* [AISecOps](https://github.com/oasiszrz/awesome-AISecOps)

## Case Studies

* [MITRE reports on in-the-wild](https://github.com/mitre/advmlthreatmatrix/blob/master/pages/case-studies-page.md#case-studies-page)

* [Avito fights content theft using adversarial attacks](https://habr.com/ru/company/avito/blog/452142/)

## Attacks based on domain

* Computer Vision

* Speech Recognition

  * Model-specific research

    * [Kaldi](https://github.com/lealeasch/adversarialattacks)

    * [Lingvo](https://github.com/yaq007/cleverhans/tree/master/examples/adversarial_asr)

    * [Deepspeech](https://arxiv.org/pdf/1801.01944)

  * Approaches

    * [Man-in-the-Elevator](https://www.usenix.org/sites/default/files/conference/protected-files/woot15_slides_vaidya.pdf)

  * Noise hiding techniques

    * [DolphinAttack](https://github.com/USSLab/DolphinAttack)

    * [MPEG Compression](https://arxiv.org/pdf/1808.05665)

## Attacks based on strategy

* Information gathering

  * [Membership inference](https://arxiv.org/pdf/1610.05820)

  * [Deanonymization](https://www.cs.utexas.edu/~shmat/shmat_oak08netflix.pdf)

  * [Model inversion](https://dl.acm.org/doi/10.1145/2810103.2813677)

  * [Model stealing](https://arxiv.org/pdf/1805.02628)

  * [Blind-spot detection](https://arxiv.org/pdf/1901.04684)

  * [State prediction](https://ieeexplore.ieee.org/document/8716085)

* Denial of Service

  * [Poisoning DoS](https://arxiv.org/pdf/1708.08689.pdf)

  * [Sponge examples](https://arxiv.org/pdf/2006.03463)

* Biometric Spoofing

  * [Master fingerprint](https://arxiv.org/pdf/1705.07386)

  * [Face recognition evasion](https://dl.acm.org/doi/10.1145/2976749.2978392)

## CTF and Hackathons

* [NIPS 2017: Defense Against Adversarial Attack](https://www.kaggle.com/c/nips-2017-defense-against-adversarial-attack/data)

* [NIPS 2018 : Adversarial Vision Challenge](https://www.crowdai.org/challenges)

* [GeekPwn CAAD 2018](http://2018.geekpwn.org/en/index.html#4).

* [IJCAI-19 Alibaba Adversarial AI Challenge](https://tianchi.aliyun.com/markets/tianchi/ijcai19_en)

* [GeekPwn CAAD 2019](http://www.geekpwn.org/zh/index.html)

* [Positive Hack Days 2019: AI CTF](https://2019.phdays.com/en/program/contests/aI-ctf/)

* [Positive Hack Days 2021: AI CTF](https://2021.phdays.com/en/program/contests/ai-track/)

* [Positive Hack Days 2022: AI CTF](https://ai.ctf.su/)

* [UTCTF 2019 (FaceSafe, Bot Protection IV tasks)](https://github.com/utisss/UTCTF-19)

* [vishwaCTF21 (Good Driver Bad Driver task)](https://vishwactf.com/)

## Frameworks

* [**adversarial-robustness-toolbox**](https://github.com/IBM/adversarial-robustness-toolbox)

* [**foolbox**](https://github.com/bethgelab/foolbox)

* [**cleverhans**](https://github.com/tensorflow/cleverhans)