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Vision-language pre-trained (VLP) models have been the foundation of numerous vision-language tasks. Given their prevalence, it becomes imperative to assess their adversarial robustness, especially when deploying them in security-crucial real-world applications. Traditionally, adversarial perturbations generated for this assessment target specific VLP models, datasets, and/or downstream tasks. This practice suffers from low transferability and additional computation costs when transitioning to new scenarios.
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Zhang et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69de77dbbf539e2270558a67 — DOI: https://doi.org/10.1145/3626772.3657781
Peng-Fei Zhang
Zi Huang
Guangdong Bai
The University of Queensland
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