Adversarial examples are one critical security threat to various visual
applications, where injected human-imperceptible perturbations can confuse the
output.Generating transferable adversarial examples in the black-box setting is
crucial but challenging in practice. Existing input-diversity-based methods
adopt different image transformations, but may be inefficient due to
insufficient input diversity and an identical perturbation step size. Motivated
by the fact that different image regions have distinctive weights in
classification, this paper proposes a black-box adversarial generative
framework by jointly designing enhanced input diversity and adaptive step
sizes. We design local mixup to randomly mix a group of transformed adversarial
images, strengthening the input diversity. For precise adversarial generation,
we project the perturbation into the $tanh$ space to relax the boundary
constraint. Moreover, the step sizes of different regions can be dynamically
adjusted by integrating a second-order momentum.Extensive experiments on
ImageNet validate that our framework can achieve superior transferability
compared to state-of-the-art baselines.

黑盒对抗生成框架通过增强输入的多样性和自适应步长来生成可转移的对抗样本，具有超过现有基准的表现。

通过局部混合和自适应步长提高对抗样本的转移性能

Boosting the Transferability of Adversarial Examples via Local Mixup and  Adaptive Step Size

This paper proposes a novel approach to adaptive step sizes in stochastic
gradient descent (SGD) by utilizing quantities that we have identified as
numerically traceable -- the Lipschitz constant for gradients and a concept of
the local variance in search directions. Our findings yield a nearly
hyperparameter-free algorithm for stochastic optimization, which has provable
convergence properties when applied to quadratic problems and exhibits truly
problem adaptive behavior on classical image classification tasks. Our
framework enables the potential inclusion of a preconditioner, thereby enabling
the implementation of adaptive step sizes for stochastic second-order
optimization methods.

该研究提出了一种新颖的自适应步长方法来解决随机梯度下降（SGD）中的问题，通过利用我们识别出的可追踪的量（梯度的 Lipschitz 常数和搜索方向的局部方差的概念），我们的发现为随机优化提供了几乎无需调参的算法，该算法在应用于二次问题时具有可证明的收敛性质，并在经典图像分类任务中展现出真正的问题自适应行为。我们的框架还可以包含预处理器，从而实现对随机二阶优化方法的自适应步长的实现。

自适应预条件化随机梯度下降的步长

Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent

Score-based (denoising diffusion) generative models have recently gained a
lot of success in generating realistic and diverse data. These approaches
define a forward diffusion process for transforming data to noise and generate
data by reversing it (thereby going from noise to data). Unfortunately, current
score-based models generate data very slowly due to the sheer number of score
network evaluations required by numerical SDE solvers.
In this work, we aim to accelerate this process by devising a more efficient
SDE solver. Existing approaches rely on the Euler-Maruyama (EM) solver, which
uses a fixed step size. We found that naively replacing it with other SDE
solvers fares poorly - they either result in low-quality samples or become
slower than EM. To get around this issue, we carefully devise an SDE solver
with adaptive step sizes tailored to score-based generative models piece by
piece. Our solver requires only two score function evaluations, rarely rejects
samples, and leads to high-quality samples. Our approach generates data 2 to 10
times faster than EM while achieving better or equal sample quality. For
high-resolution images, our method leads to significantly higher quality
samples than all other methods tested. Our SDE solver has the benefit of
requiring no step size tuning.

本文提出一种利用自适应步长的随机微分方程求解器来加速基于评分的生成模型，方法能够稳定高效地生成高质量的数据，特别是在高分辨率图像生成方面表现优异。