Answers.Body: <p>I would say this is not necessarily a duplicate but quite similar to some other questions. However, I will answer the question posed here. </p>
  
  <p>At a theoretical level, what you are asking is there any algorithm that cannot be tricked into predicting the wrong class?</p>
  
  <p>The answer is: <strong>No</strong></p>
  
  <p>It is analogous to asking whether there is a perfect architecture for an arbitrary classification problem, and that answer is quite obviously not. It would also not likely to be terribly difficult to show this is provably the case, at least for a class of algorithms(i.e connectionist models).</p>
  
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Body: <p>Is any classifier not subject to fooling as in <a href="https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf" rel="nofollow noreferrer">here</a>?</p>
  <p>My question is related to <a href="https://ai.stackexchange.com/q/6892/2444">this</a>, but not an exact duplicate.</p>
  <p>What I wanted to ask is that any classifiers inherently do not subject (or less prone) to attack. I have a feeling that non-linear classifiers should be less susceptible to attack.</p>
  
Tags: <machine-learning><models><adversarial-ml><model-request>
Title: Is any classifier not subject (or less susceptible) to fooling?
UserDisplayName: user559678
ThreadText: # Is any classifier not subject (or less susceptible) to fooling?
  
  (Asked by: username_0 on 2019-04-04)
  
  Is any classifier not subject to fooling as in [here](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf)?
  
  My question is related to [this](https://ai.stackexchange.com/q/6892/2444), but not an exact duplicate.
  
  What I wanted to ask is that any classifiers inherently do not subject (or less prone) to attack. I have a feeling that non-linear classifiers should be less susceptible to attack.
  
  **Tags:** machine-learning, models, adversarial-ml, model-request
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-04-04. Score: 1
  
  I would say this is not necessarily a duplicate but quite similar to some other questions. However, I will answer the question posed here.
  
  At a theoretical level, what you are asking is there any algorithm that cannot be tricked into predicting the wrong class?
  
  The answer is: **No**
  
  It is analogous to asking whether there is a perfect architecture for an arbitrary classification problem, and that answer is quite obviously not. It would also not likely to be terribly difficult to show this is provably the case, at least for a class of algorithms(i.e connectionist models).
  
  
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