Neural network models are vulnerable to adversarial examples, and adversarial
transferability further increases the risk of adversarial attacks. Current
methods based on transferability often rely on substitute models, which can be
impractical and costly in real-world scenarios due to the unavailability of
training data and the victim model's structural details. In this paper, we
propose a novel approach that directly constructs adversarial examples by
extracting transferable features across various tasks. Our key insight is that
adversarial transferability can extend across different tasks. Specifically, we
train a sequence-to-sequence generative model named CT-GAT using adversarial
sample data collected from multiple tasks to acquire universal adversarial
features and generate adversarial examples for different tasks. We conduct
experiments on ten distinct datasets, and the results demonstrate that our
method achieves superior attack performance with small cost.

我们提出了一种新颖的方法，通过提取不同任务间可转移的特征，直接构建对抗样本，并利用这些特征生成适用于不同任务的对抗样本，在十个不同数据集上的实验结果表明，我们的方法在攻击性能上取得了卓越的表现，并且成本较小。