<p>There are already a couple of papers in the literature that attempt to provide a taxonomy and survey of <strong>adversarial attacks</strong>. I will just list the two that I think are reliable enough that you can probably use as a reference.</p>
<ul>
<li><p><a href="https://www.sciencedirect.com/science/article/pii/S1574013718303289" rel="nofollow noreferrer">A taxonomy and survey of attacks against machine learning</a> (2019) by Nikolaos Pitropakis et al., published in <a href="https://www.sciencedirect.com/journal/computer-science-review/about/aims-and-scope" rel="nofollow noreferrer">Computer Science Review</a></p>
</li>
<li><p><a href="https://arxiv.org/pdf/1712.07107.pdf" rel="nofollow noreferrer">Adversarial Examples: Attacks and Defenses for Deep Learning</a> (2019) by Xiaoyong Yuan et al., published in <a href="https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5962385" rel="nofollow noreferrer">IEEE Transactions on Neural Networks and Learning Systems</a>.</p>
</li>
</ul>
<p>Needless to say, there are different adversarial attacks, such as the <a href="https://www.tensorflow.org/tutorials/generative/adversarial_fgsm" rel="nofollow noreferrer">Fast Gradient Signed Method (FGSM)</a>, and they can be classified into different categories, such as <strong>evasion attacks</strong> or <strong>poising attacks</strong>. You can find a lot more info in these cited papers.</p>

CC BY-SA 4.0
2020-12-09T10:47:27.253Z
2020-12-09T10:47:27.253Z
nbro
<p>I am a medical doctor working on methodological aspects of health-oriented ML. Reproducibility, replicability, generalisability are critical in this area. Among many questions, some are raised by adversarial attacks (AA).</p>
<p>My question is to be considered from a literature review point of view: suppose I want to check an algorithm from an AA point of view:</p>
<ul>
<li>is there a systematic methodology approach to be used, relating format of the data, type of models, and AA? Conceptually, is there a taxonomy of AA? If so, practically, are some AA considered as gold standards?</li>
</ul>

<machine-learning><reference-request><adversarial-ml><healthcare><taxonomy>
Is there a taxonomy of adversarial attacks?
user2478159
# Is there a taxonomy of adversarial attacks?

(Asked by: username_0 on 2020-12-08)

I am a medical doctor working on methodological aspects of health-oriented ML. Reproducibility, replicability, generalisability are critical in this area. Among many questions, some are raised by adversarial attacks (AA).

My question is to be considered from a literature review point of view: suppose I want to check an algorithm from an AA point of view:

* is there a systematic methodology approach to be used, relating format of the data, type of models, and AA? Conceptually, is there a taxonomy of AA? If so, practically, are some AA considered as gold standards?

**Tags:** machine-learning, reference-request, adversarial-ml, healthcare, taxonomy


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## 1 answers

### Answer by username_1 on 2020-12-09. Score: 2 | Accepted answer

There are already a couple of papers in the literature that attempt to provide a taxonomy and survey of **adversarial attacks**. I will just list the two that I think are reliable enough that you can probably use as a reference.

* [A taxonomy and survey of attacks against machine learning](https://www.sciencedirect.com/science/article/pii/S1574013718303289) (2019) by Nikolaos Pitropakis et al., published in [Computer Science Review](https://www.sciencedirect.com/journal/computer-science-review/about/aims-and-scope)
* [Adversarial Examples: Attacks and Defenses for Deep Learning](https://arxiv.org/pdf/1712.07107.pdf) (2019) by Xiaoyong Yuan et al., published in [IEEE Transactions on Neural Networks and Learning Systems](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5962385).

Needless to say, there are different adversarial attacks, such as the [Fast Gradient Signed Method (FGSM)](https://www.tensorflow.org/tutorials/generative/adversarial_fgsm), and they can be classified into different categories, such as **evasion attacks** or **poising attacks**. You can find a lot more info in these cited papers.


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