model-specificity/index.html at master · Trusted-System-Lab/model-specificity · GitHub

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  <title>Catch-Only-One</title>
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            <h1 class="title is-1 publication-title">
              Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization</h1>
            <div class="is-size-5 publication-authors">
              <!-- Paper authors -->
               <!-- Zihan Wang, Zhiyong Ma, Zhongkui Ma, Shuofeng Liu, Akide Liu, Derui Wang, Minhui Xue, Guangdong Bai -->
              <span class="author-block">
                <a href="https://www.zihan.com.au/" target="_blank">Zihan Wang</a><sup>1,2*</sup></span>
              <span class="author-block">
                <a target="https://www.linkedin.com/in/zhiyong-ma-b4a829322">Zhiyong (Ethan) Ma</a><sup>1*</sup>,</span>
              <span class="author-block">
                <a href="https://zhongkuima.github.io/" target="_blank">Zhongkui Ma</a><sup>1*</sup>,</span>
              <span class="author-block">
                <a target="https://shuofeng-uq.github.io/">Shuofeng Liu</a><sup>1</sup>,</span>
              <span class="author-block">
                <a target="https://scholar.google.com/citations?user=1HjSeKgAAAAJ&hl=en">Akide Liu</a><sup>3</sup>,</span>
               </br>
              <span class="author-block">
                <a target="https://neuralsec.github.io/">Derui Wang</a><sup>2</sup>,</span>
              <span class="author-block">
                <a target="https://people.csiro.au/x/j/jason-xue">Minhui Xue</a><sup>2</sup>,</span>
              <span class="author-block">
                <a target="https://baigd.github.io/">Guangdong Bai</a><sup>1</sup>,</span>
            </div>
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              <span class="author-block">
                <sup>1</sup>The University of Queensland
                <br>
                <sup>2</sup>CSIRO's Data61
                <br>
                <sup>3</sup>Monash University
              </span>
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      <h4 class="subtitle has-text-centered">Clean example (left) versus non-transferable example (right).</h4>
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            Highlights</h2>
        </div>
      </div>
      <p>
        🌟 Keeps your authorized (intended) model accurate while any other models on the same inputs fail.<br>
        🌟 Lightweight and production-friendly, able to process the entire ImageNet dataset within seconds.<br>
        🌟 Backed by strong theoretical bounds that quantify unauthorized degradation via spectral misalignment.<br>
      </p>
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  </section>

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            Motivation</h2>
        <h5 class="title is-5" style="text-align: center;">
          Recent AI regulations call for data that remain useful for innovation while resistant to misuse, balancing utility with protection at the model level. </br></br> Existing works either perturb data to make it unlearnable or retrain models to suppress transfer.
        </h5>

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            <h4 class="title is-5" style="text-align: center;">🔒 Challenges</h4>
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             Neither governs inference by unknown models, and both typically require control over training.
            </p>
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          <h4 class="title is-5" style="text-align: center;">🔑 Solutions</h4>
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              <!-- <br> -->
            Non-transferable examples (NEs), a training-free and data-agnostic input-side usage-control mechanism.
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              <h4 class="subtitle has-text-centered"><b>TO UPDATE</b></h4>
              Detailed descriptions.
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            Module 1</h2>
            <h5 class="title is-5" style="text-align: center;">
              Single sentence to summarize the first module of the paper.
            </h5>
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              <img src="static/images/ReverseCurve.png" alt="MY ALT TEXT" , width="900" />
              <h4 class="subtitle has-text-centered"><b>Caption of the figure.</b></h4>
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  <!--BibTex citation -->
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      <h2 class="title">BibTex</h2>
      <pre><code>
        @inproceedings{wang2025nontransfer,
          title={Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization},
          author={Wang, Zihan and Ma, Ethan and Ma, Zhongkui and Liu, Shuofeng and Liu, Akide and Wang, Derui and Xue, Minhui and Bai, Guangdong},
          booktitle={arXiv preprint arXiv:2510.10982},
          year={2025}
        }
      </code></pre>
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  </section>
  <!--End BibTex citation -->


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