changed if man file

_posts/2024-10-07-ifman.md

@@ -84,7 +84,7 @@ We also anticipate our attack to work better with smaller training sets, as ther
 <li><strong>Our attacks transfer when influence scores are computed with an unknown test set.</strong>When an unknown test set is used to compute influence scores, our attacks perform better as ranking $k$ increases, as shown in the figure above. This occurs because rank of the target sample, optimized with the original test set, deteriorates with the unknown test set and a larger $k$ increases the likelihood of the target still being in the top-$k$ rankings.</li>
 
 
-<li><strong>How does our Multi-Target Attack perform with changing target set size and desired ranking $k$?</strong>Intuitively, our attack should perform better when the size of the target set is larger compared to ranking $k$ -- this is simply because a larger target set offers more candidates to take the top-$k$ rankings spots, thus increasing the chances of some of them making it to top- $k$. Our experimental results confirm this intuition; as demonstrated in the figure below, we observe that (1) for a fixed value of ranking $k$, a larger target set size leads to a higher success rate; target set size of $100$ has the highest success rates for all values of ranking $k$ across the board, and (2) the success rate decreases with increasing value of $k$ for all target set sizes and datasets. These results are for the high-accuracy similarity regime where the original and manipulated model accuracy differ by less than $3\%$.</li>
+<li><strong>How does our Multi-Target Attack perform with changing target set size and desired ranking $k$?</strong> Intuitively, our attack should perform better when the size of the target set is larger compared to ranking $k$ -- this is simply because a larger target set offers more candidates to take the top-$k$ rankings spots, thus increasing the chances of some of them making it to top- $k$. Our experimental results confirm this intuition; as demonstrated in the figure below, we observe that (1) for a fixed value of ranking $k$, a larger target set size leads to a higher success rate; target set size of $100$ has the highest success rates for all values of ranking $k$ across the board, and (2) the success rate decreases with increasing value of $k$ for all target set sizes and datasets. These results are for the high-accuracy similarity regime where the original and manipulated model accuracy differ by less than $3\%$.</li>
 
 <div class='l-body' align="center">
 <img class="img-fluid rounded z-depth-1" src="{{ site.baseurl }}/assets/img/2024-10-ifman/multitarget.png">