The adversarial vulnerability of deep neural networks (DNNs) has drawn great
attention due to the security risk of applying these models in real-world
applications. Based on transferability of adversarial examples, an increasing
number of transfer-based methods have been developed to fool black-box DNN
models whose architecture and parameters are inaccessible. Although tremendous
effort has been exerted, there still lacks a standardized benchmark that could
be taken advantage of to compare these methods systematically, fairly, and
practically. Our investigation shows that the evaluation of some methods needs
to be more reasonable and more thorough to verify their effectiveness, to
avoid, for example, unfair comparison and insufficient consideration of
possible substitute/victim models. Therefore, we establish a transfer-based
attack benchmark (TA-Bench) which implements 30+ methods. In this paper, we
evaluate and compare them comprehensively on 25 popular substitute/victim
models on ImageNet. New insights about the effectiveness of these methods are
gained and guidelines for future evaluations are provided. Code at:
this https URL

该论文探讨了深度神经网络（DNNs）的对抗性脆弱性，并建立了一个基于转移的攻击基准（TA-Bench）来评估和比较 30 多种方法在 ImageNet 上的 25 个受害模型，从而提供了这些方法的有效性新见解和未来评估的指导。

系统性、实践性和公正性地评估基于传递的攻击

Towards Evaluating Transfer-based Attacks Systematically, Practically,  and Fairly

Few-shot classification is a challenging problem due to the uncertainty
caused by using few labelled samples. In the past few years, many methods have
been proposed to solve few-shot classification, among which transfer-based
methods have proved to achieve the best performance. Following this vein, in
this paper we propose a novel transfer-based method that builds on two
successive steps: 1) preprocessing the feature vectors so that they become
closer to Gaussian-like distributions, and 2) leveraging this preprocessing
using an optimal-transport inspired algorithm (in the case of transductive
settings). Using standardized vision benchmarks, we prove the ability of the
proposed methodology to achieve state-of-the-art accuracy with various
datasets, backbone architectures and few-shot settings.

本文提出了一种新颖的基于转移学习的方法，通过预处理特征向量使其更接近高斯分布，并利用基于最优输运的算法 (在普遍情况下) 加以利用，证明了该方法在各种数据集、主干体系结构和少样本情形下达到最先进的准确性。