allenhzy commited on
Commit
60eb8c9
·
1 Parent(s): 810109b
Files changed (1) hide show
  1. index.html +4 -5
index.html CHANGED
@@ -40,7 +40,6 @@
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  if (!$(this).hasClass('selected')) {
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  $('.formula').hide(200);
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- $('.eq-des').hide(200);
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  $('.formula-list > a').removeClass('selected');
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  $(this).addClass('selected');
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  var target = $(this).attr('href');
@@ -418,7 +417,7 @@
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  <h2 class="title is-3">Adaptive Attack</h2>
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  <div class="columns is-centered">
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- <div class="column container">
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  <p>
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  Attackers can design adaptive attacks to try to bypass BEYOND when the attacker knows all the parameters of the model
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  and the detection strategy. For an SSL model with a feature extractor <equation-inline>f</equation-inline>, a projector $h$, and a classification head $g$,
@@ -459,14 +458,14 @@
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  <div class="columns is-centered">
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  <div class="column container">
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- <p class="eq-des label-loss">
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  where $\displaystyle k$ represents the number of generated neighbors, $\displaystyle y_t$ is the target class, and $\displaystyle \mathcal{L}$ is the cross entropy loss function
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  </p>
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- <p class="eq-des representation-loss" style="display: none">
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  where $\displaystyle \mathcal{S}$ is the cosine similarity.
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  </p>
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- <p class="eq-des total-loss" style="display: none;">
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  where $\displaystyle \mathcal{L}_C$ indicates classifier's loss function, $\displaystyle y_t$ is the targeted class, and $\displaystyle \alpha$ refers to a hyperparameter.
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  </p>
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  </div>
 
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  if (!$(this).hasClass('selected')) {
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  $('.formula').hide(200);
 
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  $('.formula-list > a').removeClass('selected');
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  $(this).addClass('selected');
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  var target = $(this).attr('href');
 
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  <h2 class="title is-3">Adaptive Attack</h2>
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  <div class="columns is-centered">
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+ <div class="column container formula">
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  <p>
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  Attackers can design adaptive attacks to try to bypass BEYOND when the attacker knows all the parameters of the model
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  and the detection strategy. For an SSL model with a feature extractor <equation-inline>f</equation-inline>, a projector $h$, and a classification head $g$,
 
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  <div class="columns is-centered">
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  <div class="column container">
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+ <p class="formula label-loss">
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  where $\displaystyle k$ represents the number of generated neighbors, $\displaystyle y_t$ is the target class, and $\displaystyle \mathcal{L}$ is the cross entropy loss function
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  </p>
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+ <p class="formula representation-loss" style="display: none">
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  where $\displaystyle \mathcal{S}$ is the cosine similarity.
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  </p>
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+ <p class="formula total-loss" style="display: none;">
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  where $\displaystyle \mathcal{L}_C$ indicates classifier's loss function, $\displaystyle y_t$ is the targeted class, and $\displaystyle \alpha$ refers to a hyperparameter.
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  </p>
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  </div>