Deep neural networks are susceptible to adversarial attacks, which pose a
significant threat to their security and reliability in real-world
applications. The most notable adversarial attacks are transfer-based attacks,
where an adversary crafts an adversarial example to fool one model, which can
also fool other models. While previous research has made progress in improving
the transferability of untargeted adversarial examples, the generation of
targeted adversarial examples that can transfer between models remains a
challenging task. In this work, we present a novel approach to generate
transferable targeted adversarial examples by exploiting the vulnerability of
deep neural networks to perturbations on high-frequency components of images.
We observe that replacing the high-frequency component of an image with that of
another image can mislead deep models, motivating us to craft perturbations
containing high-frequency information to achieve targeted attacks. To this end,
we propose a method called Low-Frequency Adversarial Attack (\name), which
trains a conditional generator to generate targeted adversarial perturbations
that are then added to the low-frequency component of the image. Extensive
experiments on ImageNet demonstrate that our proposed approach significantly
outperforms state-of-the-art methods, improving targeted attack success rates
by a margin from 3.2\% to 15.5\%.

通过利用深度神经网络对图像高频分量的扰动的脆弱性，我们提出了一种新方法来生成可转移的有针对性对抗样本，该方法通过训练条件生成器生成针对性的对抗扰动，并将其添加到图像的低频分量中，实验证明我们的方法显著优于现有的方法，将有针对性攻击成功率提高了 3.2% 至 15.5%。

LFAA：使用低频扰动创建可转移的定向对抗样本

LFAA: Crafting Transferable Targeted Adversarial Examples with  Low-Frequency Perturbations

An intriguing property of deep neural networks is the existence of
adversarial examples, which can transfer among different architectures. These
transferable adversarial examples may severely hinder deep neural network-based
applications. Previous works mostly study the transferability using small scale
datasets. In this work, we are the first to conduct an extensive study of the
transferability over large models and a large scale dataset, and we are also
the first to study the transferability of targeted adversarial examples with
their target labels. We study both non-targeted and targeted adversarial
examples, and show that while transferable non-targeted adversarial examples
are easy to find, targeted adversarial examples generated using existing
approaches almost never transfer with their target labels. Therefore, we
propose novel ensemble-based approaches to generating transferable adversarial
examples. Using such approaches, we observe a large proportion of targeted
adversarial examples that are able to transfer with their target labels for the
first time. We also present some geometric studies to help understanding the
transferable adversarial examples. Finally, we show that the adversarial
examples generated using ensemble-based approaches can successfully attack
Clarifai.com, which is a black-box image classification system.

本文首次对大规模数据集和大型模型进行了对抗样本的可迁移性的研究，同时也是首次研究了设计有目标的对抗样本在其目标标签之间的可转移性。通过新颖的集成方法，本文发现大量有目标的对抗样本可以成功地与其目标标签一起转移，并且这些使用集成方法生成的对抗样本可以成功地攻击黑匣子图像分类系统 Clarifai.com。