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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> |
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<div class="is-size-5 publication-authors"> |
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<span class="author-block"> |
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<a href="#" target="_blank">Zhiyuan He</a><sup>1*</sup>,</span> |
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<span class="author-block"> |
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<a href="https://yangyijune.github.io/" target="_blank">Yijun Yang</a><sup>1*</sup>,</span> |
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<span class="author-block"> |
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<a href="https://sites.google.com/site/pinyuchenpage/home" target="_blank">Pin-Yu Chen</a><sup>2</sup>, |
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</span> |
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<span class="author-block"> |
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<a href="https://cure-lab.github.io/" target="_blank">Qiang Xu</a><sup>1</sup>, |
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</span> |
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<span class="author-block"> |
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<a href="https://tsungyiho.github.io/" target="_blank">Tsung-Yi Ho</a><sup>1</sup>, |
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</span> |
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<div class="is-size-5 publication-authors"> |
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<span class="author-block"><sup>*</sup>Equal contribution,</span> |
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<span class="author-block"><sup>1</sup>The Chinese University of Hong Kong,</span> |
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<span class="author-block"><sup>2</sup>IBM Research</span> |
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<span>Paper</span> |
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<a href="https://arxiv.org/abs/2209.00005" target="_blank" |
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class="external-link button is-normal is-rounded is-dark"> |
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<span>arXiv</span> |
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<h2 class="title is-3">Abstract</h2> |
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<div class="content has-text-justified"> |
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<p> |
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Deep Neural Networks (DNNs) have achieved excellent performance in various fields. However, DNNs’ vulnerability to |
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Adversarial Examples (AE) hinders their deployments to safety-critical applications. In this paper, we present <strong>BEYOND</strong>, |
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an innovative AE detection frameworkdesigned for reliable predictions. BEYOND identifies AEs by distinguishing the AE’s |
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abnormal relation with its augmented versions, i.e. neighbors, from two prospects: representation similarity and label |
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consistency. An off-the-shelf Self-Supervised Learning (SSL) model is used to extract the representation and predict the |
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label for its highly informative representation capacity compared to supervised learning models. We found clean samples |
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maintain a high degree of representation similarity and label consistency relative to their neighbors, in contrast to AEs |
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which exhibit significant discrepancies. We explain this obser vation and show that leveraging this discrepancy BEYOND can |
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accurately detect AEs. Additionally, we develop a rigorous justification for the effectiveness of BEYOND. Furthermore, as a |
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plug-and-play model, BEYOND can easily cooperate with the Adversarial Trained Classifier (ATC), achieving state-of-the-art |
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(SOTA) robustness accuracy. Experimental results show that BEYOND outperforms baselines by a large margin, especially under |
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adaptive attacks. Empowered by the robust relationship built on SSL, we found that BEYOND outperforms baselines in terms |
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of both detection ability and speed. |
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</p> |
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</div> |
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</div> |
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</div> |
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</section> |
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<section class="section"> |
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<div class="container is-max-desktop"> |
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<h2 class="title is-3">Neighborhood Relations of AEs and Clean Samples</h2> |
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<div class="columns is-centered"> |
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<div class="column container-centered"> |
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<img src="./static/images/relations.jpg" alt="Neighborhood Relations of Benign Examples and AEs"/> |
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<p> |
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<strong>Figure 1. Neighborhood Relations of AEs and Clean Samples.</strong> |
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</p> |
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</div> |
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</div> |
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<div class="columns is-centered"> |
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<div class="column has-text-justified"> |
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<p> |
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The previous method, Latent Neighbourhood Graph (LNG), represents the relationship between the input sample and the reference |
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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 |
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and reference nodes, and train a graph neural network to detect AEs. |
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</p> |
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<p> |
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In this work, We explore the relationship between inputs and their test-time augmented neighbours. As shown in Figure. 1, |
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clean samples exhibit a stronger correlation with their neighbors in terms of label consistency and representation |
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similarity. In contrast, AEs are distinctly separated from their neighbors. According to this observation, we propose <strong>BEYOND</strong> |
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to detection adversarial examples. |
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</p> |
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</div> |
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</div> |
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</div> |
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</section> |
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<section class="section"> |
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<div class="container is-max-desktop"> |
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<h2 class="title is-3">Method Overview of BEYOND</h2> |
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<div class="columns is-centered"> |
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<div class="column container-centered"> |
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<img src="./static/images/overview.png" alt="Method Overview of BEYOND"/> |
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<p><strong>Figure 2. Overview of BEYOND.</strong> First, we augment the input image to obtain a bunch of its neighbors. Then, we |
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perform the label consistency detection mechanism on the classifier’s prediction of the input image and that of neighbors predicted by |
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SSL’s classification head. Meanwhile, the representation similarity mechanism employs cosine distance to measure the similarity among |
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the input image and its neighbors. Finally, The input image with poor label consistency or representation similarity is flagged as AE.</p> |
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</div> |
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</div> |
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</div> |
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</section> |
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<section class="section"> |
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<div class="container is-max-desktop"> |
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<h2 class="title is-3">Detection Performance</h2> |
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<div class="columns is-centered"> |
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<div class="column container-centered"> |
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<table class="tg" border="1" style="width:100%;"> |
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<caption><strong>Table 1.</strong>The Area Under the ROC Curve (AUC) of Different Adversarial Detection Approaches on CIFAR-10. LNG |
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is not open-sourced and the data comes from its report. To align with baselines, classifier: ResNet110, FGSM: ε = 0.05, PGD: |
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ε = 0.02. Note that BEYOND needs no AE for training, leading to the same value on both seen and unseen settings. The <strong>bold</strong> values |
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are the best performance, and the <u><i>underlined italicized</i></u> values are the second-best performanc</caption> |
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<thead> |
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<tr> |
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<th class="tg-amwm" rowspan="2">AUC(%)</th> |
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<th class="tg-baqh" colspan="4"><span style="font-weight:bold;font-style:italic">Unse</span><span style="font-weight:bold">e</span><span style="font-weight:bold;font-style:italic">n</span><span style="font-weight:bold">: </span>Attacks used in training are preclude from tests</th> |
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<th class="tg-baqh" colspan="5"><span style="font-weight:bold;font-style:italic">Seen</span><span style="font-weight:bold">:</span> Attacks used in training are included in tests</th> |
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</tr> |
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<tr> |
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<th class="tg-baqh">FGSM</th> |
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<th class="tg-baqh">PGD</th> |
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<th class="tg-baqh">AutoAttack</th> |
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<th class="tg-baqh">Square</th> |
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<th class="tg-baqh">FGSM</th> |
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<th class="tg-baqh">PGD</th> |
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<th class="tg-baqh">CW</th> |
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<th class="tg-baqh">AutoAttack</th> |
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<th class="tg-baqh">Square</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td class="tg-baqh">DkNN</td> |
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<td class="tg-baqh">61.55</td> |
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<td class="tg-baqh">51.22</td> |
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<td class="tg-baqh">52.12</td> |
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<td class="tg-baqh">59.46</td> |
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<td class="tg-baqh">61.55</td> |
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<td class="tg-baqh">51.22</td> |
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<td class="tg-baqh">61.52</td> |
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<td class="tg-baqh">52.12</td> |
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<td class="tg-baqh">59.46</td> |
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</tr> |
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<tr> |
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<td class="tg-baqh">kNN</td> |
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<td class="tg-baqh">61.83</td> |
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<td class="tg-baqh">54.52</td> |
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<td class="tg-baqh">52.67</td> |
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<td class="tg-baqh">73.39</td> |
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<td class="tg-baqh">61.83</td> |
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<td class="tg-baqh">54.52</td> |
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<td class="tg-baqh">62.23</td> |
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<td class="tg-baqh">52.67</td> |
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<td class="tg-baqh">73.39</td> |
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</tr> |
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<tr> |
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<td class="tg-baqh">LID</td> |
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<td class="tg-baqh">71.08</td> |
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<td class="tg-baqh">61.33</td> |
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<td class="tg-baqh">55.56</td> |
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<td class="tg-baqh">66.18</td> |
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<td class="tg-baqh">73.61</td> |
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<td class="tg-baqh">67.98</td> |
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<td class="tg-baqh">55.68</td> |
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<td class="tg-baqh">56.33</td> |
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<td class="tg-baqh">85.94</td> |
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</tr> |
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<tr> |
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<td class="tg-baqh">Hu</td> |
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<td class="tg-baqh">84.51</td> |
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<td class="tg-baqh">58.59</td> |
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<td class="tg-baqh">53.55</td> |
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<td class="tg-2imo">95.82</td> |
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<td class="tg-baqh">84.51</td> |
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<td class="tg-baqh">58.59</td> |
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<td class="tg-2imo">91.02</td> |
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<td class="tg-baqh">53.55</td> |
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<td class="tg-baqh">95.82</td> |
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</tr> |
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<tr> |
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<td class="tg-baqh">Mao</td> |
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<td class="tg-baqh">95.33</td> |
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<td class="tg-2imo">82.61</td> |
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<td class="tg-2imo">81.95</td> |
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<td class="tg-baqh">85.76</td> |
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<td class="tg-baqh">95.33</td> |
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<td class="tg-baqh">82.61</td> |
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<td class="tg-baqh">83.10</td> |
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<td class="tg-baqh">81.95</td> |
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<td class="tg-baqh">85.76</td> |
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</tr> |
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<tr> |
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<td class="tg-baqh">LNG</td> |
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<td class="tg-2imo">98.51 </td> |
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<td class="tg-baqh">63.14 </td> |
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<td class="tg-baqh">58.47 </td> |
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<td class="tg-baqh">94.71 </td> |
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<td class="tg-amwm">99.88 </td> |
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<td class="tg-2imo">91.39 </td> |
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<td class="tg-baqh">89.74 </td> |
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<td class="tg-2imo">84.03 </td> |
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<td class="tg-2imo">98.82 </td> |
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</tr> |
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<tr> |
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<td class="tg-baqh">BEYOND</td> |
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<td class="tg-amwm">98.89</td> |
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<td class="tg-amwm">99.28</td> |
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<td class="tg-amwm">99.16</td> |
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<td class="tg-amwm">99.27</td> |
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<td class="tg-2imo">98.89</td> |
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<td class="tg-amwm">99.28</td> |
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<td class="tg-amwm">99.20</td> |
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<td class="tg-amwm">99.16</td> |
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<td class="tg-amwm">99.27</td> |
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</tr> |
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</tbody> |
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</table> |
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</div> |
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</div> |
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</div> |
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</section> |
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<section class="section"> |
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<div class="container is-max-desktop"> |
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<h2 class="title is-3">Adaptive Attack</h2> |
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<div class="columns is-centered"> |
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<div class="column container formula"> |
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<p> |
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Attackers can design adaptive attacks to try to bypass BEYOND when the attacker knows all the parameters of the model |
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and the detection strategy. For an SSL model with a feature extractor <i>f</i>, a projector <i>h</i>, and a classification head <i>g</i>, |
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the classification branch can be formulated as <strong>C</strong>= <i>f</i> ° <i>g</i> and the representation branch as <strong>R</strong> = <i>f</i> ° <i>h</i>. |
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To attack effectively, the adversary must deceive the target model while guaranteeing the label consistency and representation similarity of the SSL model. |
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</div> |
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</div> |
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<div class="columns is-centered"> |
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<div class="column container-centered"> |
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<div id="adaptive-loss-formula" class="container"> |
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<div id="adaptive-loss-formula-list" class="row align-items-center formula-list"> |
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<a href=".label-loss" class="selected">Label Consistency Loss</a> |
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<a href=".representation-loss">Representation Similarity Loss</a> |
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<a href=".total-loss">Total Loss</a> |
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<div style="clear: both"></div> |
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</div> |
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<div class="row align-items-center adaptive-loss-formula-content"> |
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<span class="formula label-loss formula-content"> |
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$$ |
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\displaystyle |
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Loss_{label} = \frac{1}{k} \sum_{i=1}^{k} \mathcal{L}\left(\mathbb{C}\left(W^i(x+\delta) \right), y_t\right) |
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$$ |
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</span> |
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<span class="formula representation-loss formula-content" style="display: none;"> |
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$$ |
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\displaystyle |
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Loss_{repre} = \frac{1}{k} \sum_{i=1}^{k}\mathcal{S}(\mathbb{R}(W^i(x+\delta)), \mathbb{R}(x+\delta)) |
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$$ |
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</span> |
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<span class="formula total-loss formula-content" style="display: none;"> |
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$$\displaystyle \mathcal{L}_C(x+\delta, y_t) + Loss_{label} - \alpha \cdot Loss_{repre}$$ |
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</span> |
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</div> |
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</div> |
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</div> |
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</div> |
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<div class="columns is-centered"> |
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<div class="column container adaptive-loss-formula-content"> |
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<p class="formula label-loss formula-content"> |
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where k represents the number of generated neighbors, <i>y</i><sub><i>t</i></sub> is the target class, and <strong><i>L</i></strong> is the cross entropy loss function. |
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</p> |
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<p class="formula representation-loss formula-content" style="display: none"> |
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where k represents the number of generated neighbors, and <strong><i>S</i></strong> is the cosine similarity. |
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</p> |
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<p class="formula total-loss formula-content" style="display: none;"> |
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where <strong><i>L</i></strong><sub>C</sub> indicates classifier's loss function, <i>y</i><sub><i>t</i></sub> is the targeted class, and α refers to a hyperparameter, |
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which is a trade-off parameter between label consistency and representation similarity.. |
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</p> |
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</div> |
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</div> |
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<div class="columns is-centered"> |
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<div class="column is-full-width"> |
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<h3 class="title is-4">Performance of BEYOND against Adaptive Attacks</h3> |
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<div class="content has-text-justified"> |
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<p> |
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We evaluate the detection performance of BEYOND against adaptive attacks on different datasets and show the ROC curves under different perturbation budgets as follows: |
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</p> |
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</div> |
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<div class="columns is-vcentered interpolation-panel"> |
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<div id="adaptive-dataset" class="column is-3 align-items-center" style="width: 30%;"> |
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<a href="#c10" class="selected">CIFAR-10</a> |
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<a href="#imgnet" >ImageNet</a> |
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<div style="clear: both"></div> |
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</div> |
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<div id="c10" class="column interpolation-video-column" style="width: 70%;"> |
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<div id="c10-image-wrapper" > |
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<input name="c10" class="slider is-full-width is-large is-info interpolation-slider" |
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step="1" min="0" max="6" value="0" type="range"> |
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<label for="interpolation-slider"><strong>Perturbation Budget Ε</strong> from 2/255 to 128/255</label> |
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</div> |
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<div id="imgnet" class="column interpolation-video-column" style="width: 70%; display: none;"> |
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<div id="imgnet-image-wrapper" > |
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</div> |
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<input name="imgnet" class="slider is-full-width is-large is-info interpolation-slider" |
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step="1" min="0" max="6" value="0" type="range"> |
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<label for="interpolation-slider"><strong>Perturbation Budget ε</strong> from 2/255 to 128/255</label> |
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<h2 class="title">BibTeX</h2> |
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<pre><code>@article{he2024beyond, |
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author = {Zhiyuan, He and Yijun, Yang and Pin-Yu, Chen and Qiang, Xu and Tsung-Yi, Ho}, |
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title = {Be your own neighborhood: Detecting adversarial example by the neighborhood relations built on self-supervised learning}, |
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journal = {ICML}, |
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year = {2024}, |
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