equation

index.html

@@ -420,8 +420,8 @@
       <div class="column container formula">
         <p>
           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`. 
+          and the detection strategy. For an SSL model with a feature extractor <i>f</i>, a projector <i>h</i>, and a classification head <i>g</i>, 
+          the classification branch can be formulated as <strong>C</strong>= <i>f</i> &deg <i>g</i> and the representation branch as <strong>R</strong> = <i>f</i> &deg <i>h</i>. 
           To attack effectively, the adversary must deceive the target model while guaranteeing the label consistency and representation similarity of the SSL model. 
       </div>
     </div>
@@ -459,14 +459,14 @@
     <div class="columns is-centered">
       <div class="column container adaptive-loss-formula-content">
         <p class="formula label-loss formula-content">
-          where `k` represents the number of generated neighbors, `y_t` is the target class, and `\mathcal{L}` is the cross entropy loss function.
+          where k represents the number of generated neighbors, <i>y</i><sub><i>t</i></sub> is the target class, and <strong><i>L</i></strong> is the cross entropy loss function.
         </p>
         <p class="formula representation-loss formula-content" style="display: none">
-          where `k` represents the number of generated neighbors, and `mathcal{S}` is the cosine similarity.
+          where k represents the number of generated neighbors, and <strong><i>S</i></strong> is the cosine similarity.
         </p>
           
         <p class="formula total-loss formula-content" style="display: none;">
-          where `\mathcal{L}_C` indicates classifier's loss function, `y_t` is the targeted class, and `\alpha` refers to a hyperparameter.
+          where <strong><i>L</i></strong><sub>C</sub> indicates classifier's loss function, <i>y</i><sub><i>t</i></sub> is the targeted class, and &alpha refers to a hyperparameter.
         </p>
       </div>
     </div>