adaptive

index.html

@@ -26,6 +26,19 @@
   <script src="./static/js/bulma-slider.min.js"></script>
   <script src="./static/js/index.js"></script>
 
+  <script>
+    $('#adaptive-loss-formula-list').on('click', 'a', function(e) {
+        e.preventDefault();
+        if (!$(this).hasClass('selected')) {
+            $('.formula').hide(200);
+            $('.formula-list > a').removeClass('selected');
+            $(this).addClass('selected');
+            var target = $(this).attr('href');
+            $(target).show(200);
+        }
+    });
+  </script>
+
   <style type="text/css">
     .tg  {border-collapse:collapse;border-spacing:0;}
     .tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
@@ -397,7 +410,41 @@
     <h2 class="title is-3">Adaptive Attack</h2>
     <div class="columns is-centered">
       <div class="column container-centered">
-        
+        <div id="refusal-loss-formula" class="container">
+          <div id="adaptive-loss-formula-list" class="row align-items-center formula-list">
+            <a href="#label-loss" class="selected">Label Loss</a>
+            <a href="#representation-loss">Representation Loss</a>
+            <a href="#total-loss">Total Loss</a>
+            <div style="clear: both"></div>
+          </div>
+          <div id="adaptive" class="row align-items-center">
+            <span id="label-loss" class="formula" style="">
+              $$
+              \displaystyle 
+              \begin{aligned} 
+              \phi_\theta(x)&=1-\mathbb{E}_{y \sim T_\theta(x)} JB(y)\\ 
+              JB (y) &=  \begin{cases}
+                   1 \text{, if $y$ contains any jailbreak keyword;} \\
+                   0 \text{, otherwise.}
+               \end{cases} 
+              \end{aligned}
+              $$
+            </span>
+            <span id="representation-loss" class="formula" style="display: none;">
+              $$
+              \displaystyle 
+              \begin{aligned} 
+              f_\theta(x) &=1-\frac{1}{N}\sum_{i=1}^N JB(y_i)\\ 
+              JB (y_i) &=  \begin{cases}
+                   1 \text{, if $y_i$ contains any jailbreak keyword;} \\
+                   0 \text{, otherwise.}
+               \end{cases} 
+              \end{aligned}
+              $$
+            </span>
+            <span id="total-loss" class="formula" style="display: none;">$$\displaystyle g_\theta(x)=\sum_{i=1}^P \frac{f_\theta(x\oplus \mu u_i)-f_\theta(x)}{\mu} u_i $$</span>
+          </div>
+          </div>
       </div>
     </div>