Questions tagged [adversarial-ml]
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How are networks that have high gradients and operating in linear regime more vulnerable to adversarial attacks?
At Stanford, the CS230 lecture mentioned that
"high gradients and operating in linear regime more vulnerable to adversarial attacks?"
Can someone explain how?
Video link: https://youtu.be/...
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A dataset specificaly crafted to fail CNN
Is there a dataset of images that is specifically crafted in a way that it is reasonably easy for a human, but notoriously difficult for a CNN to learn?
A couple of examples are
The set of dogs on the ...
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How do GANs learn category distributions
I'm currently getting more into the topic of GANs and Generating Models. I've understood how the Generator and Discriminator work together in optimization to generate synthetic samples.
Now I'm ...
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Basic and fundamental papers in Adversarial Attacks with code
I'm looking for basic and fundamental academic papers in Adversarial Attacks or defense. The attack or defense algorithm should be be easy to understand and the code can be found in Python. Where can ...
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Connection between GANs and adversarial learning
Is there a connection between: "Adversarial Learning" (AL) and "Generative Adversarial Networks" (GANs)? Is it valid to say that GANs employ AL?
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Creating adversarial sample against logistic regreession
I performed a binary classification using logistic regression.
My goal is the following:
I know the coefficient w of the hyperplane equation $y = wTx + b$. What I would like to do is create opposing ...
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SAGAN - what is the correct architecture?
Hi, in the original paper the following scheme of the self-attention appears:
https://arxiv.org/pdf/1805.08318.pdf
In a later overview:
https://arxiv.org/pdf/1906.01529.pdf
this scheme appears:
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Could you generate search queries to poison data analysis by a search engine?
A simple problem with search engines is that you have to trust that they will not build a profile of search queries you submit. (Without Tor or e.g. homomorphic encryption, that is.)
Suppose we put ...
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Intuitive explanation of Adversarial machine learning
How would you explain Adversarial machine learning in simple layman terms for a non-STEM person? What are the main ideas behind Adversarial machine learning?
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