Adversarial attacks have the potential to mislead deep neural network
classifiers by introducing slight perturbations. Developing algorithms that can
mitigate the effects of these attacks is crucial for ensuring the safe use of
artificial intelligence. Recent studies have suggested that score-based
diffusion models are effective in adversarial defenses. However, existing
diffusion-based defenses rely on the sequential simulation of the reversed
stochastic differential equations of diffusion models, which are
computationally inefficient and yield suboptimal results. In this paper, we
introduce a novel adversarial defense scheme named ScoreOpt, which optimizes
adversarial samples at test-time, towards original clean data in the direction
guided by score-based priors. We conduct comprehensive experiments on multiple
datasets, including CIFAR10, CIFAR100 and ImageNet. Our experimental results
demonstrate that our approach outperforms existing adversarial defenses in
terms of both robustness performance and inference speed.

我们引入了一个名为 ScoreOpt 的新的对抗防御方案，通过在测试时优化对抗样本，向原始清数据在得分优先的方向