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| <title>Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning</title> | |
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| <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> | |
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| <div class="publication-links"> | |
| <!-- PDF Link. --> | |
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| <a href="https://arxiv.org/abs/2209.00005" target="_blank" | |
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| </span> | |
| <span>Paper</span> | |
| </a> | |
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| <a href="https://arxiv.org/abs/2209.00005" target="_blank" | |
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| <span>arXiv</span> | |
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| <span>Video</span> | |
| </a> | |
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| <!-- Code Link. --> | |
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| <a href="https://github.com/google/nerfies" target="_blank" | |
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| <span>Code</span> | |
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| <h2 class="subtitle has-text-centered"> | |
| <span class="dnerf">Nerfies</span> turns selfie videos from your phone into | |
| free-viewpoint | |
| portraits. | |
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| <h2 class="title is-3">Abstract</h2> | |
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| <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> | |
| <p> | |
| Our approach augments neural radiance fields | |
| (NeRF) by optimizing an | |
| additional continuous volumetric deformation field that warps each observed point into a | |
| canonical 5D NeRF. | |
| We observe that these NeRF-like deformation fields are prone to local minima, and | |
| propose a coarse-to-fine optimization method for coordinate-based models that allows for | |
| more robust optimization. | |
| By adapting principles from geometry processing and physical simulation to NeRF-like | |
| models, we propose an elastic regularization of the deformation field that further | |
| improves robustness. | |
| </p> | |
| <p> | |
| We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie | |
| photos/videos into deformable NeRF | |
| models that allow for photorealistic renderings of the subject from arbitrary | |
| viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data | |
| using a | |
| rig with two mobile phones that take time-synchronized photos, yielding train/validation | |
| 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> --> | |
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| <!--/ Abstract. --> | |
| <!-- Paper video. --> | |
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| <h2 class="title is-3">Video</h2> | |
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| <iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&showinfo=0" | |
| frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe> | |
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| <!--/ Paper video. --> | |
| </div> | |
| </section> | |
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| <h2 class="title is-3">Introduction</h2> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section class="section"> | |
| <div class="container is-max-desktop"> | |
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| <div class="column has-text-centered"> | |
| <h2 class="title is-3">Method Overview of BEYOND</h2> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section class="section"> | |
| <div class="container is-max-desktop"> | |
| <div class="columns is-centered"> | |
| <div class="column has-text-centered"> | |
| <h2 class="title is-3">Method Overview of BEYOND</h2> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section class="section" id="BibTeX"> | |
| <div class="container is-max-desktop content"> | |
| <h2 class="title">BibTeX</h2> | |
| <pre><code>@article{he2024beyond, | |
| author = {Zhiyuan, He and Yijun, Yang and Pin-Yu, Chen and Qiang, Xu and Tsung-Yi, Ho}, | |
| title = {Be your own neighborhood: Detecting adversarial example by the neighborhood relations built on self-supervised learning}, | |
| journal = {ICML}, | |
| year = {2024}, | |
| }</code></pre> | |
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