equation

index.html

@@ -40,6 +40,7 @@
         if (!$(this).hasClass('selected')) {
 
             $('.formula').hide(200);
+            $('.eq-des').hide(200);
             $('.formula-list > a').removeClass('selected');
             $(this).addClass('selected');
             var target = $(this).attr('href');
@@ -424,11 +425,11 @@
           the classification branch can be formulated as $\mathbb{C} = f\circ g$ and the representation branch as $\mathbb{R} = f\circ h$. 
           To attack effectively, the adversary must deceive the target model while guaranteeing the label consistency and representation similarity of the SSL model. 
 
-          where $\mathcal{S}$ represents cosine similarity, $k$ represents the number of generated neighbors, 
+          <!-- where $\mathcal{S}$ represents cosine similarity, $k$ represents the number of generated neighbors, 
           and the linear augmentation function $W(x)=W(x,p);~p\sim P$ randomly samples $p$ from the parameter distribution $P$ to generate different neighbors. 
           Note that we guarantee the generated neighbors are fixed each time by fixing the random seed. The adaptive adversaries perform attacks on the following objective function:
 
-          where $\mathcal{L}_C$ indicates classifier's loss function, $y_t$ is the targeted class, and $\alpha$ refers to a hyperparameter.
+          where $\mathcal{L}_C$ indicates classifier's loss function, $y_t$ is the targeted class, and $\alpha$ refers to a hyperparameter. -->
       </div>
     </div>
 
@@ -463,19 +464,19 @@
 
       <div class="columns is-centered">
         <div class="column">
-          <p id="label-loss">
+          <p id="label-loss" class="eq-des">
             Attackers can design adaptive attacks to try to bypass BEYOND when the attacker knows all the parameters of the model 
             and the detection strategy. For an SSL model with a feature extractor $f$, a projector $h$, and a classification head $g$, 
             the classification branch can be formulated as $\mathbb{C} = f\circ g$ and the representation branch as $\mathbb{R} = f\circ h$. 
             To attack effectively, the adversary must deceive the target model while guaranteeing the label consistency and representation similarity of the SSL model. 
           </p>
-          <p id="representation-loss", style="display: none">
+          <p id="representation-loss" class="eq-des" style="display: none">
             where $\mathcal{S}$ represents cosine similarity, $k$ represents the number of generated neighbors, 
             and the linear augmentation function $W(x)=W(x,p);~p\sim P$ randomly samples $p$ from the parameter distribution $P$ to generate different neighbors. 
             Note that we guarantee the generated neighbors are fixed each time by fixing the random seed. The adaptive adversaries perform attacks on the following objective function:
           </p>
             
-          <p id="total-loss", style="display: none;">
+          <p id="total-loss" class="eq-des" style="display: none;">
             where $\mathcal{L}_C$ indicates classifier's loss function, $y_t$ is the targeted class, and $\alpha$ refers to a hyperparameter.
           </p>
         </div>