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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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</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 &alpha refers to a hyperparameter
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</p>
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</div>
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</div>
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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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</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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