<p>I think, one could generate a data set, with a random variable in each component of the data vector, add this data to the training data set, and then shuffle the combined data set.</p>

CC BY-SA 4.0
2020-03-10T01:00:10.883Z
Can you please clarify how this answers the question?
nbro
CC BY-SA 4.0
2020-03-09T16:01:34.377Z
2020-03-09T16:01:34.377Z
Jerry B
<p>I'm working on Adversarial Machine Learning, and have read multiple papers on this topic, some of them are mentioned as follows:</p>

<ul>
<li>Poisoning Attacks on SVMs: <a href="https://arxiv.org/pdf/1206.6389.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1206.6389.pdf</a></li>
<li>Adversarial Label Flips on Support Vector Machines</li>
</ul>

<p>However, I am not able to find any literature on data poisoning for SVMs using Manifold regularization. Is there anyone who has knowledge about that?</p>

CC BY-SA 4.0
2020-03-08T06:46:24.103Z
I found the following Youtube video: [Poisoning Attacks against Support Vector Machines](https://www.youtube.com/watch?v=8dBasP-rhlY). Is it close to what you are looking for?
CoffeeBaconAddict
<machine-learning><regularization><support-vector-machine><adversarial-ml><causation>
How do I poison an SVM with manifold regularization?
boomselector
# How do I poison an SVM with manifold regularization?

(Asked by: username_0 on 2020-02-18)

I'm working on Adversarial Machine Learning, and have read multiple papers on this topic, some of them are mentioned as follows:

* Poisoning Attacks on SVMs: <https://arxiv.org/pdf/1206.6389.pdf>
* Adversarial Label Flips on Support Vector Machines

However, I am not able to find any literature on data poisoning for SVMs using Manifold regularization. Is there anyone who has knowledge about that?

**Tags:** machine-learning, regularization, support-vector-machine, adversarial-ml, causation

> **Comments:**
> **username_1**: I found the following Youtube video: [Poisoning Attacks against Support Vector Machines](https://www.youtube.com/watch?v=8dBasP-rhlY). Is it close to what you are looking for?  

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## 1 answers

### Answer by username_2 on 2020-03-09. Score: 0

I think, one could generate a data set, with a random variable in each component of the data vector, add this data to the training data set, and then shuffle the combined data set.

> **Comments:**
> **username_3**: Can you please clarify how this answers the question?  

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