Efforts to improve the adversarial robustness of convolutional neural networks have primarily focused on developing more effective adversarial training methods. In contrast, little attention was devoted to analyzing the role of architectural elements (such as topology, depth, and width) on adversarial robustness. This paper seeks to bridge this gap and present a holistic study on the impact of architectural design on adversarial robustness. We focus on residual networks and consider architecture design at the block level, i.e., topology, kernel size, activation, and normalization, as well as at the network scaling level, i.e., depth and width of each block in the network. In both cases, we first derive insights through systematic ablative experiments. Then we design a robust residual block, dubbed RobustResBlock, and a compound scaling rule, dubbed RobustScaling, to distribute depth and width at the desired FLOP count. Finally, we combine RobustResBlock and RobustScaling and present a portfolio of adversarially robust residual networks, RobustResNets, spanning a broad spectrum of model capacities. Experimental validation across multiple datasets and adversarial attacks demonstrate that RobustResNets consistently outperform both the standard WRNs and other existing robust architectures, achieving state-of-the-art AutoAttack robust accuracy of 61.1% without additional data and 63.7% with 500K external data while being $2\times$ more compact in terms of parameters. Code is available at \url{ https://github.com/zhichao-lu/robust-residual-network}

已知深神经网络（DNN）容易受到对抗性攻击的影响。已经提出了一系列防御方法来培训普遍稳健的DNN，其中对抗性培训已经证明了有希望的结果。然而，尽管对对抗性培训开发的初步理解，但从架构角度来看，它仍然不明确，从架构角度来看，什么配置可以导致更强大的DNN。在本文中，我们通过全面调查网络宽度和深度对前对方培训的DNN的鲁棒性的全面调查来解决这一差距。具体地，我们进行以下关键观察：1）更多参数（更高的模型容量）不一定有助于对抗冒险; 2）网络的最后阶段（最后一组块）降低能力实际上可以改善对抗性的鲁棒性; 3）在相同的参数预算下，存在对抗性鲁棒性的最佳架构配置。我们还提供了一个理论分析，解释了为什么这种网络配置可以帮助鲁棒性。这些架构见解可以帮助设计对抗的强制性DNN。代码可用于\ url {https://github.com/hanxunh/robustwrn}。

在本文中，我们询问视觉变形金刚（VIT）是否可以作为改善机器学习模型对抗逃避攻击的对抗性鲁棒性的基础结构。尽管较早的作品集中在改善卷积神经网络上，但我们表明VIT也非常适合对抗训练以实现竞争性能。我们使用自定义的对抗训练配方实现了这一目标，该配方是在Imagenet数据集的一部分上使用严格的消融研究发现的。与卷积相比，VIT的规范培训配方建议强大的数据增强，部分是为了补偿注意力模块的视力归纳偏置。我们表明，该食谱在用于对抗训练时可实现次优性能。相比之下，我们发现省略所有重型数据增强，并添加一些额外的零件（$ \ varepsilon $ -Warmup和更大的重量衰减），从而大大提高了健壮的Vits的性能。我们表明，我们的配方在完整的Imagenet-1k上概括了不同类别的VIT体系结构和大规模模型。此外，调查了模型鲁棒性的原因，我们表明，在使用我们的食谱时，在训练过程中产生强烈的攻击更加容易，这会在测试时提高鲁棒性。最后，我们通过提出一种量化对抗性扰动的语义性质并强调其与模型的鲁棒性的相关性来进一步研究对抗训练的结果。总体而言，我们建议社区应避免将VIT的规范培训食谱转换为在对抗培训的背景下进行强大的培训和重新思考常见的培训选择。

Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense. Among these methods, adversarial training has been drawing increasing attention because of its simplicity and effectiveness. However, the performance of the adversarial training is greatly limited by the architectures of target DNNs, which often makes the resulting DNNs with poor accuracy and unsatisfactory robustness. To address this problem, we propose DSARA to automatically search for the neural architectures that are accurate and robust after adversarial training. In particular, we design a novel cell-based search space specially for adversarial training, which improves the accuracy and the robustness upper bound of the searched architectures by carefully designing the placement of the cells and the proportional relationship of the filter numbers. Then we propose a two-stage search strategy to search for both accurate and robust neural architectures. At the first stage, the architecture parameters are optimized to minimize the adversarial loss, which makes full use of the effectiveness of the adversarial training in enhancing the robustness. At the second stage, the architecture parameters are optimized to minimize both the natural loss and the adversarial loss utilizing the proposed multi-objective adversarial training method, so that the searched neural architectures are both accurate and robust. We evaluate the proposed algorithm under natural data and various adversarial attacks, which reveals the superiority of the proposed method in terms of both accurate and robust architectures. We also conclude that accurate and robust neural architectures tend to deploy very different structures near the input and the output, which has great practical significance on both hand-crafting and automatically designing of accurate and robust neural architectures.

Adversarial training (AT) is one of the most effective ways for improving the robustness of deep convolution neural networks (CNNs). Just like common network training, the effectiveness of AT relies on the design of basic network components. In this paper, we conduct an in-depth study on the role of the basic ReLU activation component in AT for robust CNNs. We find that the spatially-shared and input-independent properties of ReLU activation make CNNs less robust to white-box adversarial attacks with either standard or adversarial training. To address this problem, we extend ReLU to a novel Sparta activation function (Spatially attentive and Adversarially Robust Activation), which enables CNNs to achieve both higher robustness, i.e., lower error rate on adversarial examples, and higher accuracy, i.e., lower error rate on clean examples, than the existing state-of-the-art (SOTA) activation functions. We further study the relationship between Sparta and the SOTA activation functions, providing more insights about the advantages of our method. With comprehensive experiments, we also find that the proposed method exhibits superior cross-CNN and cross-dataset transferability. For the former, the adversarially trained Sparta function for one CNN (e.g., ResNet-18) can be fixed and directly used to train another adversarially robust CNN (e.g., ResNet-34). For the latter, the Sparta function trained on one dataset (e.g., CIFAR-10) can be employed to train adversarially robust CNNs on another dataset (e.g., SVHN). In both cases, Sparta leads to CNNs with higher robustness than the vanilla ReLU, verifying the flexibility and versatility of the proposed method.

The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. Among them, adversarial training is the most promising one, which flattens the input loss landscape (loss change with respect to input) via training on adversarially perturbed examples. However, how the widely used weight loss landscape (loss change with respect to weight) performs in adversarial training is rarely explored. In this paper, we investigate the weight loss landscape from a new perspective, and identify a clear correlation between the flatness of weight loss landscape and robust generalization gap. Several well-recognized adversarial training improvements, such as early stopping, designing new objective functions, or leveraging unlabeled data, all implicitly flatten the weight loss landscape. Based on these observations, we propose a simple yet effective Adversarial Weight Perturbation (AWP) to explicitly regularize the flatness of weight loss landscape, forming a double-perturbation mechanism in the adversarial training framework that adversarially perturbs both inputs and weights. Extensive experiments demonstrate that AWP indeed brings flatter weight loss landscape and can be easily incorporated into various existing adversarial training methods to further boost their adversarial robustness.

对抗性训练遭受了稳健的过度装备，这是一种现象，在训练期间鲁棒测试精度开始减少。在本文中，我们专注于通过使用常见的数据增强方案来减少强大的过度装备。我们证明，与先前的发现相反，当与模型重量平均结合时，数据增强可以显着提高鲁棒精度。此外，我们比较各种增强技术，并观察到空间组合技术适用于对抗性培训。最后，我们评估了我们在Cifar-10上的方法，而不是$ \ ell_ indty $和$ \ ell_2 $ norm-indeded扰动分别为尺寸$ \ epsilon = 8/255 $和$ \ epsilon = 128/255 $。与以前的最先进的方法相比，我们表现出+ 2.93％的绝对改善+ 2.93％，+ 2.16％。特别是，反对$ \ ell_ infty $ norm-indeded扰动尺寸$ \ epsilon = 8/255 $，我们的模型达到60.07％的强劲准确性而不使用任何外部数据。我们还通过这种方法实现了显着的性能提升，同时使用其他架构和数据集如CiFar-100，SVHN和TinyimageNet。

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layerdeep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at https: //github.com/szagoruyko/wide-residual-networks.

对抗性例子的现象说明了深神经网络最基本的漏洞之一。在推出这一固有的弱点的各种技术中，对抗性训练已成为学习健壮模型的最有效策略。通常，这是通过平衡强大和自然目标来实现的。在这项工作中，我们旨在通过执行域不变的功能表示，进一步优化鲁棒和标准准确性之间的权衡。我们提出了一种新的对抗训练方法，域不变的对手学习（DIAL），该方法学习了一个既健壮又不变的功能表示形式。拨盘使用自然域及其相应的对抗域上的域对抗神经网络（DANN）的变体。在源域由自然示例组成和目标域组成的情况下，是对抗性扰动的示例，我们的方法学习了一个被限制的特征表示，以免区分自然和对抗性示例，因此可以实现更强大的表示。拨盘是一种通用和模块化技术，可以轻松地将其纳入任何对抗训练方法中。我们的实验表明，将拨号纳入对抗训练过程中可以提高鲁棒性和标准精度。

深度神经网络很容易被称为对抗攻击的小扰动都愚弄。对抗性培训（AT）是一种近似解决了稳健的优化问题，以最大限度地减少最坏情况损失，并且被广泛认为是对这种攻击的最有效的防御。由于产生了强大的对抗性示例的高计算时间，已经提出了单步方法来减少培训时间。然而，这些方法遭受灾难性的过度装备，在训练期间侵犯准确度下降。虽然提出了改进，但它们增加了培训时间和稳健性远非多步骤。我们为FW优化（FW-AT）开发了对抗的对抗培训的理论框架，揭示了损失景观与$ \ ell_2 $失真之间的几何连接。我们分析地表明FW攻击的高变形相当于沿攻击路径的小梯度变化。然后在各种深度神经网络架构上进行实验证明，$ \ ell \ infty $攻击对抗强大的模型实现近乎最大的$ \ ell_2 $失真，而标准网络具有较低的失真。此外，实验表明，灾难性的过度拟合与FW攻击的低变形强烈相关。为了展示我们理论框架的效用，我们开发FW-AT-Adap，这是一种新的逆势训练算法，它使用简单的失真度量来调整攻击步骤的数量，以提高效率而不会影响鲁棒性。 FW-AT-Adapt提供培训时间以单步快速分配方法，并改善了在白色盒子和黑匣子设置中的普发内精度的最小损失和多步PGD之间的差距。

在过去的几年中，卷积神经网络（CNN）一直是广泛的计算机视觉任务中的主导神经架构。从图像和信号处理的角度来看，这一成功可能会令人惊讶，因为大多数CNN的固有空间金字塔设计显然违反了基本的信号处理法，即在其下采样操作中对定理进行采样。但是，由于不良的采样似乎不影响模型的准确性，因此在模型鲁棒性开始受到更多关注之前，该问题已被广泛忽略。最近的工作[17]在对抗性攻击和分布变化的背景下，毕竟表明，CNN的脆弱性与不良下降采样操作引起的混叠伪像之间存在很强的相关性。本文以这些发现为基础，并引入了一个可混合的免费下采样操作，可以轻松地插入任何CNN体系结构：频lowcut池。我们的实验表明，结合简单而快速的FGSM对抗训练，我们的超参数无操作员显着提高了模型的鲁棒性，并避免了灾难性的过度拟合。

改善深度神经网络（DNN）对抗对抗示例的鲁棒性是安全深度学习的重要而挑战性问题。跨越现有的防御技术，具有预计梯度体面（PGD）的对抗培训是最有效的。对手训练通过最大化分类丢失，通过最大限度地减少从内在最大化生成的逆势示例的丢失来解决\ excepitient {内部最大化}生成侵略性示例的初始最大优化问题。 。因此，衡量内部最大化的衡量标准是如何对对抗性培训至关重要的。在本文中，我们提出了这种标准，即限制优化（FOSC）的一阶静止条件，以定量评估内部最大化中发现的对抗性实例的收敛质量。通过FOSC，我们发现，为了确保更好的稳健性，必须在培训的\ Texit {稍后的阶段}中具有更好的收敛质量的对抗性示例。然而，在早期阶段，高收敛质量的对抗例子不是必需的，甚至可能导致稳健性差。基于这些观察，我们提出了一种\ Texit {动态}培训策略，逐步提高产生的对抗性实例的收敛质量，这显着提高了对抗性培训的鲁棒性。我们的理论和经验结果表明了该方法的有效性。

积极调查深度神经网络的对抗鲁棒性。然而，大多数现有的防御方法限于特定类型的对抗扰动。具体而言，它们通常不能同时为多次攻击类型提供抵抗力，即，它们缺乏多扰动鲁棒性。此外，与图像识别问题相比，视频识别模型的对抗鲁棒性相对未开发。虽然有几项研究提出了如何产生对抗性视频，但在文献中只发表了关于防御策略的少数关于防御策略的方法。在本文中，我们提出了用于视频识别的多种抗逆视频的第一战略之一。所提出的方法称为Multibn，使用具有基于学习的BN选择模块的多个独立批量归一化（BN）层对多个对冲视频类型进行对抗性训练。利用多个BN结构，每个BN Brach负责学习单个扰动类型的分布，从而提供更精确的分布估计。这种机制有利于处理多种扰动类型。 BN选择模块检测输入视频的攻击类型，并将其发送到相应的BN分支，使MultiBN全自动并允许端接训练。与目前的对抗训练方法相比，所提出的Multibn对不同甚至不可预见的对抗性视频类型具有更强的多扰动稳健性，从LP界攻击和物理上可实现的攻击范围。在不同的数据集和目标模型上保持真实。此外，我们进行了广泛的分析，以研究多BN结构的性质。

Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples-inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. 1

最近，Wong等人。表明，使用单步FGSM的对抗训练导致一种名为灾难性过度拟合（CO）的特征故障模式，其中模型突然变得容易受到多步攻击的影响。他们表明，在FGSM（RS-FGSM）之前添加随机扰动似乎足以防止CO。但是，Andriushchenko和Flammarion观察到RS-FGSM仍会导致更大的扰动，并提出了一个昂贵的常规化器（Gradalign），DEMATER（GARGALIGN）DES昂贵（Gradalign）Dust Forrasiniger（Gradalign）Dust co避免在这项工作中，我们有条不紊地重新审视了噪声和剪辑在单步对抗训练中的作用。与以前的直觉相反，我们发现在干净的样品周围使用更强烈的噪声与不剪接相结合在避免使用大扰动半径的CO方面非常有效。基于这些观察结果，我们提出了噪声-FGSM（N-FGSM），尽管提供了单步对抗训练的好处，但在大型实验套件上没有经验分析，这表明N-FGSM能够匹配或超越以前的单步方法的性能，同时达到3 $ \ times $加速。代码可以在https://github.com/pdejorge/n-fgsm中找到

积极的数据增强是视觉变压器（VIT）的强大泛化能力的关键组成部分。一种这样的数据增强技术是对抗性培训;然而，许多先前的作品表明，这通常会导致清洁的准确性差。在这项工作中，我们展示了金字塔对抗训练，这是一种简单有效的技术来提高韦维尔的整体性能。我们将其与“匹配”辍学和随机深度正则化配对，这采用了干净和对抗样品的相同辍学和随机深度配置。类似于Advprop的CNNS的改进（不直接适用于VIT），我们的金字塔对抗性训练会破坏分销准确性和vit和相关架构的分配鲁棒性之间的权衡。当Imagenet-1K数据训练时，它导致ImageNet清洁准确性的182美元的vit-B模型的精确度，同时由7美元的稳健性指标同时提高性能，从$ 1.76 \％$至11.45 \％$。我们为Imagenet-C（41.4 MCE），Imagenet-R（$ 53.92 \％$），以及Imagenet-Sketch（41.04美元\％$）的新的最先进，只使用vit-b / 16骨干和我们的金字塔对抗训练。我们的代码将在接受时公开提供。

In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular Effi-cientNet models while being up to 5× faster on GPUs.

In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in constructing CNNs. We propose a dynamic selection mechanism in CNNs that allows each neuron to adaptively adjust its receptive field size based on multiple scales of input information. A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer. Multiple SK units are stacked to a deep network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art architectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input. The code and models are available at https://github.com/implus/SKNet.

标准化技术已成为现代卷积神经网络（Convnets）中的基本组件。特别是，许多最近的作品表明，促进重量的正交性有助于培训深层模型并提高鲁棒性。对于Courmnets，大多数现有方法基于惩罚或归一化矩阵判断或施加卷积核的重量矩阵。这些方法经常摧毁或忽视核的良性卷积结构;因此，对于深扫描器来说，它们通常是昂贵或不切实际的。相比之下，我们介绍了一种简单富有高效的“卷积归一化”（ConvNORM）方法，可以充分利用傅立叶域中的卷积结构，并用作简单的即插即用模块，以方便地结合到任何围栏中。我们的方法是通过最近关于卷积稀疏编码的预处理方法的工作启发，可以有效地促进每个层的频道方向等距。此外，我们表明我们的判断可以降低重量矩阵的层状频谱标准，从而改善网络的嘴唇，导致培训更容易培训和改善深扫描器的鲁棒性。在噪声损坏和生成的对抗网络（GAN）下应用于分类，我们表明CONVNOMOL提高了常见扫描仪（如RENET和GAN性能）的稳健性。我们通过Cifar和Imagenet的数值实验验证了我们的研究结果。

视觉识别的“咆哮20S”开始引入视觉变压器（VITS），这将被取代的Cummnets作为最先进的图像分类模型。另一方面，vanilla vit，当应用于一般计算机视觉任务等对象检测和语义分割时面临困难。它是重新引入多个ConvNet Priors的等级变压器（例如，Swin变压器），使变压器实际上可作为通用视觉骨干网，并在各种视觉任务上展示了显着性能。然而，这种混合方法的有效性仍然在很大程度上归功于变压器的内在优越性，而不是卷积的固有感应偏差。在这项工作中，我们重新审视设计空间并测试纯粹的Convnet可以实现的限制。我们逐渐“现代化”标准Reset朝着视觉变压器的设计设计，并发现几个有助于沿途绩效差异的关键组件。此探索的结果是一个纯粹的ConvNet型号被称为ConvNext。完全由标准的Convnet模块构建，ConvNexts在准确性和可扩展性方面与变压器竞争，实现了87.8％的ImageNet Top-1精度和表现优于COCO检测和ADE20K分割的Swin变压器，同时保持了标准Convnet的简单性和效率。