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allenhzy commited on Jun 3

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@@ -3,10 +3,10 @@
 <head>
   <meta charset="utf-8">
   <meta name="description"
-        content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
-  <meta name="keywords" content="Nerfies, D-NeRF, NeRF">
   <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>Nerfies: Deformable Neural Radiance Fields</title>
   <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
         rel="stylesheet">
@@ -33,39 +33,34 @@
     <div class="container is-max-desktop">
       <div class="columns is-centered">
         <div class="column has-text-centered">
-          <h1 class="title is-1 publication-title">Nerfies: Deformable Neural Radiance Fields</h1>
           <div class="is-size-5 publication-authors">
             <span class="author-block">
-              <a href="https://keunhong.com" target="_blank">Keunhong Park</a><sup>1</sup>,</span>
             <span class="author-block">
-              <a href="https://utkarshsinha.com" target="_blank">Utkarsh Sinha</a><sup>2</sup>,</span>
             <span class="author-block">
-              <a href="https://jonbarron.info" target="_blank">Jonathan T. Barron</a><sup>2</sup>,
             </span>
             <span class="author-block">
-              <a href="http://sofienbouaziz.com" target="_blank">Sofien Bouaziz</a><sup>2</sup>,
             </span>
             <span class="author-block">
-              <a href="https://www.danbgoldman.com" target="_blank">Dan B Goldman</a><sup>2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="https://homes.cs.washington.edu/~seitz/" target="_blank">Steven M. Seitz</a><sup>1,2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="http://www.ricardomartinbrualla.com" target="_blank">Ricardo Martin-Brualla</a><sup>2</sup>
             </span>
           </div>
           <div class="is-size-5 publication-authors">
-            <span class="author-block"><sup>1</sup>University of Washington,</span>
-            <span class="author-block"><sup>2</sup>Google Research</span>
           </div>
           <div class="column has-text-centered">
             <div class="publication-links">
               <!-- PDF Link. -->
               <span class="link-block">
-                <a href="https://arxiv.org/pdf/2011.12948" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="fas fa-file-pdf"></i>
@@ -74,7 +69,7 @@
                 </a>
               </span>
               <span class="link-block">
-                <a href="https://arxiv.org/abs/2011.12948" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="ai ai-arxiv"></i>
@@ -83,7 +78,7 @@
                 </a>
               </span>
               <!-- Video Link. -->
-              <span class="link-block">
                 <a href="https://www.youtube.com/watch?v=MrKrnHhk8IA" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
@@ -91,9 +86,9 @@
                   </span>
                   <span>Video</span>
                 </a>
-              </span>
               <!-- Code Link. -->
-              <span class="link-block">
                 <a href="https://github.com/google/nerfies" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
@@ -101,16 +96,7 @@
                   </span>
                   <span>Code</span>
                   </a>
-              </span>
-              <!-- Dataset Link. -->
-              <span class="link-block">
-                <a href="https://github.com/google/nerfies/releases/tag/0.1" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="far fa-images"></i>
-                  </span>
-                  <span>Data</span>
-                  </a>
             </div>
           </div>
@@ -120,7 +106,7 @@
   </div>
 </section>
-<section class="hero teaser">
   <div class="container is-max-desktop">
     <div class="hero-body">
       <video id="teaser" autoplay muted loop playsinline height="100%">
@@ -134,10 +120,10 @@
       </h2>
     </div>
   </div>
-</section>
-<section class="hero is-light is-small">
   <div class="hero-body">
     <div class="container">
       <div id="results-carousel" class="carousel results-carousel">
@@ -192,7 +178,7 @@
       </div>
     </div>
   </div>
-</section>
 <section class="section">
@@ -203,6 +189,21 @@
         <h2 class="title is-3">Abstract</h2>
         <div class="content has-text-justified">
           <p>
             We present the first method capable of photorealistically reconstructing a non-rigidly
             deforming scene using photos/videos captured casually from mobile phones.
           </p>
@@ -228,14 +229,14 @@
             images of the same pose at different viewpoints. We show that our method faithfully
             reconstructs non-rigidly deforming scenes and reproduces unseen views with high
             fidelity.
-          </p>
         </div>
       </div>
     </div>
     <!--/ Abstract. -->
     <!-- Paper video. -->
-    <div class="columns is-centered has-text-centered">
       <div class="column is-four-fifths">
         <h2 class="title is-3">Video</h2>
         <div class="publication-video">
@@ -243,157 +244,21 @@
                   frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
         </div>
       </div>
-    </div>
     <!--/ Paper video. -->
   </div>
 </section>
-<section class="section">
-  <div class="container is-max-desktop">
-    <div class="columns is-centered">
-      <!-- Visual Effects. -->
-      <div class="column">
-        <div class="content">
-          <h2 class="title is-3">Visual Effects</h2>
-          <p>
-            Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
-            would be impossible without nerfies since it would require going through a wall.
-          </p>
-          <video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/dollyzoom-stacked.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-      </div>
-      <!--/ Visual Effects. -->
-      <!-- Matting. -->
-      <div class="column">
-        <h2 class="title is-3">Matting</h2>
-        <div class="columns is-centered">
-          <div class="column content">
-            <p>
-              As a byproduct of our method, we can also solve the matting problem by ignoring
-              samples that fall outside of a bounding box during rendering.
-            </p>
-            <video id="matting-video" controls playsinline height="100%">
-              <source src="./static/videos/matting.mp4"
-                      type="video/mp4">
-            </video>
-          </div>
-        </div>
-      </div>
-    </div>
-    <!--/ Matting. -->
-    <!-- Animation. -->
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Animation</h2>
-        <!-- Interpolating. -->
-        <h3 class="title is-4">Interpolating states</h3>
-        <div class="content has-text-justified">
-          <p>
-            We can also animate the scene by interpolating the deformation latent codes of two input
-            frames. Use the slider here to linearly interpolate between the left frame and the right
-            frame.
-          </p>
-        </div>
-        <div class="columns is-vcentered interpolation-panel">
-          <div class="column is-3 has-text-centered">
-            <img src="./static/images/interpolate_start.jpg"
-                 class="interpolation-image"
-                 alt="Interpolate start reference image."/>
-            <p>Start Frame</p>
-          </div>
-          <div class="column interpolation-video-column">
-            <div id="interpolation-image-wrapper">
-              Loading...
-            </div>
-            <input class="slider is-fullwidth is-large is-info"
-                   id="interpolation-slider"
-                   step="1" min="0" max="100" value="0" type="range">
-          </div>
-          <div class="column is-3 has-text-centered">
-            <img src="./static/images/interpolate_end.jpg"
-                 class="interpolation-image"
-                 alt="Interpolation end reference image."/>
-            <p class="is-bold">End Frame</p>
-          </div>
-        </div>
-        <br/>
-        <!--/ Interpolating. -->
-        <!-- Re-rendering. -->
-        <h3 class="title is-4">Re-rendering the input video</h3>
-        <div class="content has-text-justified">
-          <p>
-            Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
-            viewpoint such as a stabilized camera by playing back the training deformations.
-          </p>
-        </div>
-        <div class="content has-text-centered">
-          <video id="replay-video"
-                 controls
-                 muted
-                 preload
-                 playsinline
-                 width="75%">
-            <source src="./static/videos/replay.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <!--/ Re-rendering. -->
-      </div>
-    </div>
-    <!--/ Animation. -->
-    <!-- Concurrent Work. -->
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Related Links</h2>
-        <div class="content has-text-justified">
-          <p>
-            There's a lot of excellent work that was introduced around the same time as ours.
-          </p>
-          <p>
-            <a href="https://arxiv.org/abs/2104.09125" target="_blank">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
-          </p>
-          <p>
-            <a href="https://www.albertpumarola.com/research/D-NeRF/index.html" target="_blank">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/" target="_blank">NR-NeRF</a>
-            both use deformation fields to model non-rigid scenes.
-          </p>
-          <p>
-            Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/" target="_blank">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/" target="_blank">NSFF</a>, and <a href="https://neural-3d-video.github.io/" target="_blank">DyNeRF</a>
-          </p>
-          <p>
-            There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/" target="_blank">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF" target="_blank">Yen-Chen Lin's curated list of NeRF papers</a>.
-          </p>
-        </div>
-      </div>
-    </div>
-    <!--/ Concurrent Work. -->
-  </div>
-</section>
 <section class="section" id="BibTeX">
   <div class="container is-max-desktop content">
     <h2 class="title">BibTeX</h2>
-    <pre><code>@article{park2021nerfies,
-  author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
-  title     = {Nerfies: Deformable Neural Radiance Fields},
-  journal   = {ICCV},
-  year      = {2021},
 }</code></pre>
   </div>
 </section>

 <head>
   <meta charset="utf-8">
   <meta name="description"
+        content="Demo Page of BEYOND ICML 2024.">
+  <meta name="keywords" content="BEYOND, Adversarial Examples, Adversarial Detection">
   <meta name="viewport" content="width=device-width, initial-scale=1">
+  <title>Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning</title>
   <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
         rel="stylesheet">
     <div class="container is-max-desktop">
       <div class="columns is-centered">
         <div class="column has-text-centered">
+          <h1 class="title is-1 publication-title">Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning</h1>
           <div class="is-size-5 publication-authors">
             <span class="author-block">
+              <a href="#" target="_blank">Zhiyuan He</a><sup>1*</sup>,</span>
             <span class="author-block">
+              <a href="https://yangyijune.github.io/" target="_blank">Yijun Yang</a><sup>1*</sup>,</span>
             <span class="author-block">
+              <a href="https://sites.google.com/site/pinyuchenpage/home" target="_blank">Pin-Yu Chen</a><sup>2</sup>,
             </span>
             <span class="author-block">
+              <a href="https://cure-lab.github.io/" target="_blank">Qiang Xu</a><sup>1</sup>,
             </span>
             <span class="author-block">
+              <a href="https://tsungyiho.github.io/" target="_blank">Tsung-Yi Ho</a><sup>1</sup>,
             </span>
           </div>
           <div class="is-size-5 publication-authors">
+            <span class="author-block"><sup>*</sup>Equal contribution,</span>
+            <span class="author-block"><sup>1</sup>The Chinese University of Hong Kong,</span>
+            <span class="author-block"><sup>2</sup>IBM Research</span>
           </div>
           <div class="column has-text-centered">
             <div class="publication-links">
               <!-- PDF Link. -->
               <span class="link-block">
+                <a href="https://arxiv.org/abs/2209.00005" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="fas fa-file-pdf"></i>
                 </a>
               </span>
               <span class="link-block">
+                <a href="https://arxiv.org/abs/2209.00005" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="ai ai-arxiv"></i>
                 </a>
               </span>
               <!-- Video Link. -->
+              <!-- <span class="link-block">
                 <a href="https://www.youtube.com/watch?v=MrKrnHhk8IA" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                   </span>
                   <span>Video</span>
                 </a>
+              </span> -->
               <!-- Code Link. -->
+              <!-- <span class="link-block">
                 <a href="https://github.com/google/nerfies" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                   </span>
                   <span>Code</span>
                   </a>
+              </span> -->
             </div>
           </div>
   </div>
 </section>
+<!-- <section class="hero teaser">
   <div class="container is-max-desktop">
     <div class="hero-body">
       <video id="teaser" autoplay muted loop playsinline height="100%">
       </h2>
     </div>
   </div>
+</section> -->
+<!-- <section class="hero is-light is-small">
   <div class="hero-body">
     <div class="container">
       <div id="results-carousel" class="carousel results-carousel">
       </div>
     </div>
   </div>
+</section> -->
 <section class="section">
         <h2 class="title is-3">Abstract</h2>
         <div class="content has-text-justified">
           <p>
+            Deep Neural Networks (DNNs) have achieved excellent performance in various fields. However, DNNs’ vulnerability to
+            Adversarial Examples (AE) hinders their deployments to safety-critical applications. In this paper, we present <strong>BEYOND</strong>,
+            an innovative AE detection frameworkdesigned for reliable predictions. BEYOND identifies AEs by distinguishing the AE’s
+            abnormal relation with its augmented versions, i.e. neighbors, from two prospects: representation similarity and label
+            consistency. An off-the-shelf Self-Supervised Learning (SSL) model is used to extract the representation and predict the
+            label for its highly informative representation capacity compared to supervised learning models. We found clean samples
+            maintain a high degree of representation similarity and label consistency relative to their neighbors, in contrast to AEs
+            which exhibit significant discrepancies. We explain this obser vation and show that leveraging this discrepancy BEYOND can
+            accurately detect AEs. Additionally, we develop a rigorous justification for the effectiveness of BEYOND. Furthermore, as a
+            plug-and-play model, BEYOND can easily cooperate with the Adversarial Trained Classifier (ATC), achieving state-of-the-art
+            (SOTA) robustness accuracy. Experimental results show that BEYOND outperforms baselines by a large margin, especially under
+            adaptive attacks. Empowered by the robust relationship built on SSL, we found that BEYOND outperforms baselines in terms
+            of both detection ability and speed
+          </p>
+          <!-- <p>
             We present the first method capable of photorealistically reconstructing a non-rigidly
             deforming scene using photos/videos captured casually from mobile phones.
           </p>
             images of the same pose at different viewpoints. We show that our method faithfully
             reconstructs non-rigidly deforming scenes and reproduces unseen views with high
             fidelity.
+          </p> -->
         </div>
       </div>
     </div>
     <!--/ Abstract. -->
     <!-- Paper video. -->
+    <!-- <div class="columns is-centered has-text-centered">
       <div class="column is-four-fifths">
         <h2 class="title is-3">Video</h2>
         <div class="publication-video">
                   frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
         </div>
       </div>
+    </div> -->
     <!--/ Paper video. -->
   </div>
 </section>
 <section class="section" id="BibTeX">
   <div class="container is-max-desktop content">
     <h2 class="title">BibTeX</h2>
+    <pre><code>@article{he2024beyond,
+  author    = {He, Zhiyuan and Yijun, YANG and Chen, Pin-Yu and Xu, Qiang and Ho, Tsung-Yi},
+  title     = {Be your own neighborhood: Detecting adversarial example by the neighborhood relations built on self-supervised learning},
+  journal   = {ICML},
+  year      = {2024},
 }</code></pre>
   </div>
 </section>