While the untargeted black-box transferability of adversarial perturbations
has been extensively studied before, changing an unseen model's decisions to a
specific `targeted' class remains a challenging feat. In this paper, we propose
a new generative approach for highly transferable targeted perturbations
(\ours). We note that the existing methods are less suitable for this task due
to their reliance on class-boundary information that changes from one model to
another, thus reducing transferability. In contrast, our approach matches the
perturbed image `distribution' with that of the target class, leading to high
targeted transferability rates. To this end, we propose a new objective
function that not only aligns the global distributions of source and target
images, but also matches the local neighbourhood structure between the two
domains. Based on the proposed objective, we train a generator function that
can adaptively synthesize perturbations specific to a given input. Our
generative approach is independent of the source or target domain labels, while
consistently performs well against state-of-the-art methods on a wide range of
attack settings. As an example, we achieve $32.63\%$ target transferability
from (an adversarially weak) VGG19$_{BN}$ to (a strong) WideResNet on ImageNet
val. set, which is 4$\times$ higher than the previous best generative attack
and 16$\times$ better than instance-specific iterative attack. Code is
available at: {\smallhttps://github.com/Muzammal-Naseer/TTP}.

该论文提出了一种新的生成方法 —— 高度可转移的有目标扰动（TTP），该方法不依赖于源或目标域标签，能够自适应地合成特定于给定输入的扰动，并且在广泛的攻击设置下始终表现优异。

生成可转移的有针对性扰动

On Generating Transferable Targeted Perturbations

Concise, accurate descriptions of physical systems through their conserved
quantities abound in the natural sciences. In data science, however, current
research often focuses on regression problems, without routinely incorporating
additional assumptions about the system that generated the data. Here, we
propose to explore a particular type of underlying structure in the data:
Hamiltonian systems, where an "energy" is conserved. Given a collection of
observations of such a Hamiltonian system over time, we extract phase space
coordinates and a Hamiltonian function of them that acts as the generator of
the system dynamics. The approach employs an autoencoder neural network
component to estimate the transformation from observations to the phase space
of a Hamiltonian system. An additional neural network component is used to
approximate the Hamiltonian function on this constructed space, and the two
components are trained jointly. As an alternative approach, we also demonstrate
the use of Gaussian processes for the estimation of such a Hamiltonian. After
two illustrative examples, we extract an underlying phase space as well as the
generating Hamiltonian from a collection of movies of a pendulum. The approach
is fully data-driven, and does not assume a particular form of the Hamiltonian
function.

本文提出一种全数据驱动的方法，利用自编码神经网络组件估计哈密顿系统的相空间，通过另一个神经网络组件来逼近其哈密顿函数并在两个组件之间进行联合训练，提取了摆锤的相空间和生成哈密顿函数。