到目前为止对抗训练是抵御对抗例子的最有效的策略。然而，由于每个训练步骤中的迭代对抗性攻击，它遭受了高的计算成本。最近的研究表明，通过随机初始化执行单步攻击，可以实现快速的对抗训练。然而，这种方法仍然落后于稳定性和模型稳健性的最先进的对手训练算法。在这项工作中，我们通过观察随机平滑的随机初始化来更好地优化内部最大化问题，对快速对抗培训进行新的理解。在这种新的视角之后，我们还提出了一种新的初始化策略，向后平滑，进一步提高单步强大培训方法的稳定性和模型稳健性。多个基准测试的实验表明，我们的方法在使用更少的训练时间（使用相同的培训计划时，使用更少的培训时间（$ \ sim $ 3x改进）时，我们的方法达到了类似的模型稳健性。

Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters.Our "free" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks. The code is available at https://github.com/ashafahi/free_adv_train.

对抗性培训（AT）已成为一种广泛认可的防御机制，以提高深度神经网络对抗对抗攻击的鲁棒性。它解决了最小的最大优化问题，其中最小化器（即，后卫）寻求稳健的模型，以最小化由最大化器（即，攻击者）制成的对抗示例存在的最坏情况训练损失。然而，Min-Max的性质在计算密集并因此难以扩展。同时，快速算法，实际上，许多最近改进的算法，通过替换基于简单的单次梯度标志的攻击生成步骤来简化基于最大化步骤的最小值。虽然易于实施，快速缺乏理论保证，其实际表现可能是不令人满意的，患有强大的对手训练时的鲁棒性灾难性过度。在本文中，我们从双级优化（BLO）的角度来看，旨在快速设计。首先，首先进行关键观察，即快速at的最常用的算法规范等同于使用一些梯度下降型算法来解决涉及符号操作的双级问题。然而，标志操作的离散性使得难以理解算法的性能。基于上述观察，我们提出了一种新的遗传性双层优化问题，设计和分析了一组新的算法（快速蝙蝠）。 FAST-BAT能够捍卫基于符号的投影梯度下降（PGD）攻击，而无需调用任何渐变标志方法和明确的鲁棒正则化。此外，我们经验证明，通过在不诱导鲁棒性灾难性过度的情况下实现卓越的模型稳健性，或患有任何标准精度损失的稳健性，我们的方法优于最先进的快速基线。

虽然多步逆势培训被广泛流行作为对抗强烈的对抗攻击的有效防御方法，但其计算成本与标准培训相比，其计算成本是众所周知的。已经提出了几种单步侵权培训方法来减轻上述开销费用;但是，根据优化设置，它们的性能并不能充分可靠。为了克服这些限制，我们偏离了现有的基于输入空间的对抗性培训制度，并提出了一种单步潜在培训方法（SLAT），其利用潜在的代表梯度作为潜在的对抗扰动。我们证明，与所采用的潜伏扰动，恢复局部线性度并确保与现有的单步逆势训练方法相比，恢复局部线性度并确保可靠性的特征梯度的L1规范。因为潜伏的扰动基于可以在输入梯度计算过程中免费获得的潜在表示的梯度，所以所提出的方法与快速梯度标志方法相当成本。实验结果表明，尽管其结构简单，但优于最先进的加速的对抗训练方法。

The field of defense strategies against adversarial attacks has significantly grown over the last years, but progress is hampered as the evaluation of adversarial defenses is often insufficient and thus gives a wrong impression of robustness. Many promising defenses could be broken later on, making it difficult to identify the state-of-the-art. Frequent pitfalls in the evaluation are improper tuning of hyperparameters of the attacks, gradient obfuscation or masking. In this paper we first propose two extensions of the PGD-attack overcoming failures due to suboptimal step size and problems of the objective function. We then combine our novel attacks with two complementary existing ones to form a parameter-free, computationally affordable and user-independent ensemble of attacks to test adversarial robustness. We apply our ensemble to over 50 models from papers published at recent top machine learning and computer vision venues. In all except one of the cases we achieve lower robust test accuracy than reported in these papers, often by more than 10%, identifying several broken defenses.

深度神经网络（DNN）容易受到对抗性示例的影响，其中DNN由于含有不可察觉的扰动而被误导为虚假输出。对抗性训练是一种可靠有效的防御方法，可能会大大减少神经网络的脆弱性，并成为强大学习的事实上的标准。尽管许多最近的作品实践了以数据为中心的理念，例如如何生成更好的对抗性示例或使用生成模型来产生额外的培训数据，但我们回顾了模型本身，并从深度特征分布的角度重新审视对抗性的鲁棒性有见地的互补性。在本文中，我们建议分支正交性对抗训练（BORT）获得最先进的性能，仅使用原始数据集用于对抗训练。为了练习我们整合多个正交解决方案空间的设计思想，我们利用一个简单明了的多分支神经网络，可消除对抗性攻击而不会增加推理时间。我们启发提出相应的损耗函数，分支 - 正交丢失，以使多支出模型正交的每个溶液空间。我们分别在CIFAR-10，CIFAR-100和SVHN上评估了我们的方法，分别针对\ ell _ {\ infty}的规范触发尺寸\ epsilon = 8/255。进行了详尽的实验，以表明我们的方法超出了所有最新方法，而无需任何技巧。与所有不使用其他数据进行培训的方法相比，我们的模型在CIFAR-10和CIFAR-100上实现了67.3％和41.5％的鲁棒精度（在最先进的ART上提高了 +7.23％和 +9.07％ ）。我们还使用比我们的训练组胜过比我们的方法的表现要大得多。我们所有的模型和代码均可在https://github.com/huangd1999/bort上在线获得。

Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversarial examples via a first-order method like projected gradient decent (PGD). In this paper, we make the surprising discovery that it is possible to train empirically robust models using a much weaker and cheaper adversary, an approach that was previously believed to be ineffective, rendering the method no more costly than standard training in practice. Specifically, we show that adversarial training with the fast gradient sign method (FGSM), when combined with random initialization, is as effective as PGD-based training but has significantly lower cost. Furthermore we show that FGSM adversarial training can be further accelerated by using standard techniques for efficient training of deep networks, allowing us to learn a robust CIFAR10 classifier with 45% robust accuracy to PGD attacks with = 8/255 in 6 minutes, and a robust ImageNet classifier with 43% robust accuracy at = 2/255 in 12 hours, in comparison to past work based on "free" adversarial training which took 10 and 50 hours to reach the same respective thresholds. Finally, we identify a failure mode referred to as "catastrophic overfitting" which may have caused previous attempts to use FGSM adversarial training to fail. All code for reproducing the experiments in this paper as well as pretrained model weights are at https://github.com/locuslab/fast_adversarial.

作为反对攻击的最有效的防御方法之一，对抗性训练倾向于学习包容性的决策边界，以提高深度学习模型的鲁棒性。但是，由于沿对抗方向的边缘的大幅度和不必要的增加，对抗性训练会在自然实例和对抗性示例之间引起严重的交叉，这不利于平衡稳健性和自然准确性之间的权衡。在本文中，我们提出了一种新颖的对抗训练计划，以在稳健性和自然准确性之间进行更好的权衡。它旨在学习一个中度包容的决策边界，这意味着决策边界下的自然示例的边缘是中等的。我们称此方案为中等边缘的对抗训练（MMAT），该方案生成更细粒度的对抗示例以减轻交叉问题。我们还利用了经过良好培训的教师模型的逻辑来指导我们的模型学习。最后，MMAT在Black-Box和White-Box攻击下都可以实现高自然的精度和鲁棒性。例如，在SVHN上，实现了最新的鲁棒性和自然精度。

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.

It is common practice in deep learning to use overparameterized networks and train for as long as possible; there are numerous studies that show, both theoretically and empirically, that such practices surprisingly do not unduly harm the generalization performance of the classifier. In this paper, we empirically study this phenomenon in the setting of adversarially trained deep networks, which are trained to minimize the loss under worst-case adversarial perturbations. We find that overfitting to the training set does in fact harm robust performance to a very large degree in adversarially robust training across multiple datasets (SVHN, CIFAR-10, CIFAR-100, and ImageNet) and perturbation models ( ∞ and 2 ). Based upon this observed effect, we show that the performance gains of virtually all recent algorithmic improvements upon adversarial training can be matched by simply using early stopping. We also show that effects such as the double descent curve do still occur in adversarially trained models, yet fail to explain the observed overfitting. Finally, we study several classical and modern deep learning remedies for overfitting, including regularization and data augmentation, and find that no approach in isolation improves significantly upon the gains achieved by early stopping. All code for reproducing the experiments as well as pretrained model weights and training logs can be found at https://github.com/ locuslab/robust_overfitting.

Adversarial training is widely used to improve the robustness of deep neural networks to adversarial attack. However, adversarial training is prone to overfitting, and the cause is far from clear. This work sheds light on the mechanisms underlying overfitting through analyzing the loss landscape w.r.t. the input. We find that robust overfitting results from standard training, specifically the minimization of the clean loss, and can be mitigated by regularization of the loss gradients. Moreover, we find that robust overfitting turns severer during adversarial training partially because the gradient regularization effect of adversarial training becomes weaker due to the increase in the loss landscapes curvature. To improve robust generalization, we propose a new regularizer to smooth the loss landscape by penalizing the weighted logits variation along the adversarial direction. Our method significantly mitigates robust overfitting and achieves the highest robustness and efficiency compared to similar previous methods. Code is available at https://github.com/TreeLLi/Combating-RO-AdvLC.

对抗训练（AT）在防御对抗例子方面表现出色。最近的研究表明，示例对于AT期间模型的最终鲁棒性并不同样重要，即，所谓的硬示例可以攻击容易表现出比对最终鲁棒性的鲁棒示例更大的影响。因此，保证硬示例的鲁棒性对于改善模型的最终鲁棒性至关重要。但是，定义有效的启发式方法来寻找辛苦示例仍然很困难。在本文中，受到信息瓶颈（IB）原则的启发，我们发现了一个具有高度共同信息及其相关的潜在表示的例子，更有可能受到攻击。基于此观察，我们提出了一种新颖有效的对抗训练方法（Infoat）。鼓励Infoat找到具有高相互信息的示例，并有效利用它们以提高模型的最终鲁棒性。实验结果表明，与几种最先进的方法相比，Infoat在不同数据集和模型之间达到了最佳的鲁棒性。

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。

为了应对对抗性实例的威胁，对抗性培训提供了一种有吸引力的选择，可以通过在线增强的对抗示例中的培训模型提高模型稳健性。然而，大多数现有的对抗训练方法通过强化对抗性示例来侧重于提高鲁棒的准确性，但忽略了天然数据和对抗性实施例之间的增加，导致自然精度急剧下降。为了维持自然和强大的准确性之间的权衡，我们从特征适应的角度缓解了转变，并提出了一种特征自适应对抗训练（FAAT），这些培训（FAAT）跨越自然数据和对抗示例优化类条件特征适应。具体而言，我们建议纳入一类条件鉴别者，以鼓励特征成为（1）类鉴别的和（2）不变导致对抗性攻击的变化。新型的FAAT框架通过在天然和对抗数据中产生具有类似分布的特征来实现自然和强大的准确性之间的权衡，并实现从类鉴别特征特征中受益的更高的整体鲁棒性。在各种数据集上的实验表明，FAAT产生更多辨别特征，并对最先进的方法表现有利。代码在https://github.com/visionflow/faat中获得。

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

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。

单步逆势培训（AT）受到了广泛的关注，因为它被证明是有效和健壮的。然而，存在严重的灾难性过度问题，即反对投影梯度下降（PGD）攻击的强劲准确性突然下降到培训期间的0.5美元。在本文中，我们从优化的新角度来看，首先揭示每个样品和过度装箱的快速增长梯度之间的密切联系，这也可以应用于了解多步骤中的稳健的过度拟合现象。为了控制培训期间梯度的增长，我们提出了一种新的方法，子空间对抗训练（子AT），限制了仔细提取的子空间。它成功地解决了两种过度装备，因此显着提高了鲁棒性。在子空间中，我们还允许单步合并较大的步骤和更大的半径，从而进一步提高了鲁棒性性能。因此，我们实现了最先进的单步性能：我们的纯单步可以达到超过$ \ mathbf {51} \％$鲁棒准确性，反对强大的PGD-50攻击以半径8美元/ CiFar-10上的255美元，甚至超过了标准的多步PGD-10，具有巨大的计算优势。代码已释放$ \脚注{\ url {https://github.com/nblt/sub -at}} $。

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。

对抗性训练（AT）已被证明可以通过利用对抗性示例进行训练来有效地改善模型鲁棒性。但是，大多数方法面对昂贵的时间和计算成本，用于在生成对抗性示例的多个步骤中计算梯度。为了提高训练效率，快速梯度符号方法（FGSM）在方法中仅通过计算一次来快速地采用。不幸的是，鲁棒性远非令人满意。初始化的方式可能引起一个原因。现有的快速在通常使用随机的样本不合时宜的初始化，这促进了效率，但会阻碍进一步的稳健性改善。到目前为止，快速AT中的初始化仍未广泛探索。在本文中，我们以样本依赖性的对抗初始化（即，来自良性图像条件的生成网络的输出及其来自目标网络的梯度信息的输出）快速增强。随着生成网络和目标网络在训练阶段共同优化，前者可以适应相对于后者的有效初始化，从而激发了逐渐改善鲁棒性。在四个基准数据库上进行的实验评估证明了我们所提出的方法比在方法上快速的最先进方法的优越性，以及与方法相当的鲁棒性。该代码在https://github.com//jiaxiaojunqaq//fgsm-sdi上发布。

Any classifier can be "smoothed out" under Gaussian noise to build a new classifier that is provably robust to $\ell_2$-adversarial perturbations, viz., by averaging its predictions over the noise via randomized smoothing. Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversarial) robustness has been well evidenced in the literature: i.e., increasing the robustness of a classifier for an input can be at the expense of decreased accuracy for some other inputs. In this paper, we propose a simple training method leveraging this trade-off to obtain robust smoothed classifiers, in particular, through a sample-wise control of robustness over the training samples. We make this control feasible by using "accuracy under Gaussian noise" as an easy-to-compute proxy of adversarial robustness for an input. Specifically, we differentiate the training objective depending on this proxy to filter out samples that are unlikely to benefit from the worst-case (adversarial) objective. Our experiments show that the proposed method, despite its simplicity, consistently exhibits improved certified robustness upon state-of-the-art training methods. Somewhat surprisingly, we find these improvements persist even for other notions of robustness, e.g., to various types of common corruptions.