@inproceedings{
yin2024embracing,
title={Embracing Adaptation: An Effective Dynamic Defense Strategy Against Adversarial Examples},
author={Shenglin Yin and kelu Yao and Zhen Xiao and Jieyi Long},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=0MiogCwsVo}
}
Existing defense methods against adversarial examples are static, meaning that they remain unchanged once trained, regardless of changes in the attack. Consequently, static defense methods are highly vulnerable to adaptive attacks. We contend that in order to defend against more powerful attacks, the model should continuously adapt to cope with various attack methods. We propose a novel dynamic defense approach that optimizes the input by generating pseudo-labels. Subsequently, it utilizes information maximization and enhanced average prediction as optimization objectives, followed by hierarchical optimization methods to effectively counteract adversarial examples through model parameter optimization. Importantly, our approach is implemented during the inference phase and does not necessitate model retraining. It can be readily applied to existing adversarially trained models, significantly enhancing the robustness of various models against white-box, black-box, and adaptive attacks across diverse datasets. We have conducted extensive experiments to validate the state-of-the-art of our proposed method.
Embracing Adaptation: An Effective Dynamic Defense Strategy Against Adversarial Examples
Shenglin Yin kelu Yao Zhen Xiao Jieyi Long