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           sample as a graph, whose nodes are embeddings extracted by DNN and edges are built according to distances between the input node 
           and reference nodes, and train a graph neural network to detect AEs.
         </p>
+
+        <p>
+          In this work, We explore the relationship between inputs and their test-time augmented neighbours. As shown in Figure. 1, 
+          clean samples exhibit a stronger correlation with their neighbors in terms of label consistency and representation
+          similarity. In contrast, AEs are distinctly separated from their neighbors. According to this observation, we propose <strong>BEYOND</strong>
+          to detection adversarial examples.
+        </p>
       </div>
     </div>
   </div>