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October 3, 2025Open Access

Randomized Smoothing Meets Vision-Language Models

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Randomized smoothing enhances robustness certification in generative models, indicating effectiveness against adversarial attacks.

Randomized smoothing for generative models leads to effective robustness certification.
Utilizing oracle classifiers connects generative outputs to classification tasks in machine learning models.
The study shows a valid correspondence between sample numbers and certified robustness radius.
Improved scaling laws validate that fewer samples can maintain minimal loss in robustness certification.

Seferis et al. (2025) studied this question.

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October 3, 2025Open Access

View Full Paper

Ask AI

Bookmark

Authors

Randomized smoothing enhances robustness certification in generative models, indicating effectiveness against adversarial attacks.

Randomized smoothing for generative models leads to effective robustness certification.
Utilizing oracle classifiers connects generative outputs to classification tasks in machine learning models.
The study shows a valid correspondence between sample numbers and certified robustness radius.
Improved scaling laws validate that fewer samples can maintain minimal loss in robustness certification.

Seferis et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark