黑匣子逆势攻击中的一个主要问题是硬盘标签攻击设置中的高查询复杂性，其中仅提供前1个预测标签。在本文中，我们提出了一种新的基于几何方法，称为切线攻击（TA），其识别位于决策边界上的虚拟半球的最佳切线，以降低攻击的失真。假设决策边界是本地平整的，我们理论上证明了最小$ \ ell_2 $失真可以通过沿着每次迭代中的这种切线的切线线路达到决策边界来获得。为了提高我们方法的稳健性，我们进一步提出了一种通过半椭圆体取代半球的广义方法，以适应弯曲的决策边界。我们的方法是免费的Quand参数和预训练。在ImageNet和CiFar-10数据集上进行的广泛实验表明，我们的方法只能消耗少量查询来实现低幅度失真。实施源代码在https://github.com/machanic/tangentattack上在线发布。

The goal of a decision-based adversarial attack on a trained model is to generate adversarial examples based solely on observing output labels returned by the targeted model. We develop HopSkipJumpAttack, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision boundary. The proposed family includes both untargeted and targeted attacks optimized for 2 and ∞ similarity metrics respectively. Theoretical analysis is provided for the proposed algorithms and the gradient direction estimate. Experiments show HopSkipJumpAttack requires significantly fewer model queries than several state-of-the-art decision-based adversarial attacks. It also achieves competitive performance in attacking several widely-used defense mechanisms.

为了适用于现实情况，提出了边界攻击（BAS），并仅使用决策信息确保了100％的攻击成功率。但是，现有的BA方法通过利用简单的随机抽样（SRS）来估算梯度来制作对抗性示例，从而消耗大量模型查询。为了克服SRS的弊端，本文提出了基于拉丁超立方体采样的边界攻击（LHS-BA）以节省查询预算。与SR相比，LHS在相同数量的随机样品中具有更好的均匀性。因此，这些随机样品的平均值比SRS估计的平均梯度更接近真实梯度。在包括MNIST，CIFAR和IMAGENET-1K在内的基准数据集上进行了各种实验。实验结果表明，就查询效率而言，拟议的LHS-BA优于最先进的BA方法。源代码可在https://github.com/gzhu-dvl/lhs-ba上公开获得。

虽然深度神经网络在各种任务中表现出前所未有的性能，但对对抗性示例的脆弱性阻碍了他们在安全关键系统中的部署。许多研究表明，即使在黑盒设置中也可能攻击，其中攻击者无法访问目标模型的内部信息。大多数黑匣子攻击基于查询，每个都可以获得目标模型的输入输出，并且许多研究侧重于减少所需查询的数量。在本文中，我们注意了目标模型的输出完全对应于查询输入的隐含假设。如果将某些随机性引入模型中，它可以打破假设，因此，基于查询的攻击可能在梯度估计和本地搜索中具有巨大的困难，这是其攻击过程的核心。从这种动机来看，我们甚至观察到一个小的添加剂输入噪声可以中和大多数基于查询的攻击和名称这个简单但有效的方法小噪声防御（SND）。我们分析了SND如何防御基于查询的黑匣子攻击，并展示其与CIFAR-10和ImageNet数据集的八种最先进的攻击有效性。即使具有强大的防御能力，SND几乎保持了原始的分类准确性和计算速度。通过在推断下仅添加一行代码，SND很容易适用于预先训练的模型。

基于决策攻击对现实世界应用程序构成严重威胁，因为它将目标模型视为黑盒子，并且仅访问硬预测标签。最近已经努力减少查询的数量;然而，现有的基于决策攻击仍需要数千个疑问以产生良好的质量的对抗性示例。在这项工作中，我们发现一个良性样本，当前和下一个逆势示例可以自然地构建子空间中的三角形以获得任何迭代攻击。基于诸如SINES的规律，我们提出了一种新颖的三角形攻击（TA）来通过利用较长侧总是与任何三角形的较大角度相对的几何信息来优化扰动。然而，直接在输入图像上施加这样的信息是无效的，因为它不能彻底探索高维空间中输入样本的邻域。为了解决这个问题，TA优化低频空间中的扰动，以获得由于此类几何特性的一般性而有效减少。对ImageNet DataSet的广泛评估表明，TA在1,000个查询中实现了更高的攻击成功率，并且需要更少的查询，以在各种扰动预算下实现相同的攻击成功率，而不是现有的基于决策攻击。具有如此高的效率，我们进一步展示了TA在真实世界API上的适用性，即腾讯云API。

机器学习模型严重易于来自对抗性示例的逃避攻击。通常，对逆势示例的修改输入类似于原始输入的修改输入，在WhiteBox设置下由对手的WhiteBox设置构成，完全访问模型。然而，最近的攻击已经显示出使用BlackBox攻击的对逆势示例的查询号显着减少。特别是，警报是从越来越多的机器学习提供的经过培训的模型的访问界面中利用分类决定作为包括Google，Microsoft，IBM的服务提供商，并由包含这些模型的多种应用程序使用的服务提供商来利用培训的模型。对手仅利用来自模型的预测标签的能力被区别为基于决策的攻击。在我们的研究中，我们首先深入潜入最近的ICLR和SP的最先进的决策攻击，以突出发现低失真对抗采用梯度估计方法的昂贵性质。我们开发了一种强大的查询高效攻击，能够避免在梯度估计方法中看到的嘈杂渐变中的局部最小和误导中的截留。我们提出的攻击方法，ramboattack利用随机块坐标下降的概念来探索隐藏的分类器歧管，针对扰动来操纵局部输入功能以解决梯度估计方法的问题。重要的是，ramboattack对对对手和目标类别可用的不同样本输入更加强大。总的来说，对于给定的目标类，ramboattack被证明在实现给定查询预算的较低失真时更加强大。我们使用大规模的高分辨率ImageNet数据集来策划我们的广泛结果，并在GitHub上开源我们的攻击，测试样本和伪影。

In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. Specifically, by treating the attack on each benign example as one task, we develop a meta-learning framework by training a meta-generator to produce perturbations conditioned on benign examples. When attacking a new benign example, the meta generator can be quickly fine-tuned based on the feedback information of the new task as well as a few historical attacks to produce effective perturbations. Moreover, since the meta-train procedure consumes many queries to learn a generalizable generator, we utilize model-level adversarial transferability to train the meta-generator on a white-box surrogate model, then transfer it to help the attack against the target model. The proposed framework with the two types of adversarial transferability can be naturally combined with any off-the-shelf query-based attack methods to boost their performance, which is verified by extensive experiments.

许多最先进的ML模型在各种任务中具有优于图像分类的人类。具有如此出色的性能，ML模型今天被广泛使用。然而，存在对抗性攻击和数据中毒攻击的真正符合ML模型的稳健性。例如，Engstrom等人。证明了最先进的图像分类器可以容易地被任意图像上的小旋转欺骗。由于ML系统越来越纳入安全性和安全敏感的应用，对抗攻击和数据中毒攻击构成了相当大的威胁。本章侧重于ML安全的两个广泛和重要的领域：对抗攻击和数据中毒攻击。

Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern on the robustness to adversarial examples, especially for security-critical tasks such as traffic sign identification for autonomous driving. Studies have unveiled the vulnerability of a well-trained DNN by demonstrating the ability of generating barely noticeable (to both human and machines) adversarial images that lead to misclassification. Furthermore, researchers have shown that these adversarial images are highly transferable by simply training and attacking a substitute model built upon the target model, known as a black-box attack to DNNs.Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples. We use zeroth order stochastic coordinate descent along with dimension reduction, hierarchical attack and importance sampling techniques to * Pin-Yu Chen and Huan Zhang contribute equally to this work.

We propose the Square Attack, a score-based black-box l2and l∞-adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. Square Attack is based on a randomized search scheme which selects localized squareshaped updates at random positions so that at each iteration the perturbation is situated approximately at the boundary of the feasible set. Our method is significantly more query efficient and achieves a higher success rate compared to the state-of-the-art methods, especially in the untargeted setting. In particular, on ImageNet we improve the average query efficiency in the untargeted setting for various deep networks by a factor of at least 1.8 and up to 3 compared to the recent state-ofthe-art l∞-attack of Al-Dujaili & OReilly (2020). Moreover, although our attack is black-box, it can also outperform gradient-based white-box attacks on the standard benchmarks achieving a new state-of-the-art in terms of the success rate. The code of our attack is available at https://github.com/max-andr/square-attack.

随着现实世界图像的大小不同，机器学习模型是包括上游图像缩放算法的较大系统的一部分。在本文中，我们研究了基于决策的黑框设置中图像缩放过程的漏洞与机器学习模型之间的相互作用。我们提出了一种新颖的采样策略，以端到端的方式使黑框攻击利用漏洞在缩放算法，缩放防御和最终的机器学习模型中。基于这种缩放感知的攻击，我们揭示了大多数现有的缩放防御能力在下游模型的威胁下无效。此外，我们从经验上观察到，标准的黑盒攻击可以通过利用脆弱的缩放程序来显着提高其性能。我们进一步在具有基于决策的黑盒攻击的商业图像分析API上证明了这个问题。

评估防御模型的稳健性是对抗对抗鲁棒性研究的具有挑战性的任务。僵化的渐变，先前已经发现了一种梯度掩蔽，以许多防御方法存在并导致鲁棒性的错误信号。在本文中，我们确定了一种更细微的情况，称为不平衡梯度，也可能导致过高的对抗性鲁棒性。当边缘损耗的一个术语的梯度主导并将攻击朝向次优化方向推动时，发生不平衡梯度的现象。为了利用不平衡的梯度，我们制定了分解利润率损失的边缘分解（MD）攻击，并通过两阶段过程分别探讨了这些术语的攻击性。我们还提出了一个Multared和Ensemble版本的MD攻击。通过调查自2018年以来提出的17个防御模型，我们发现6种型号易受不平衡梯度的影响，我们的MD攻击可以减少由最佳基线独立攻击评估的鲁棒性另外2％。我们还提供了对不平衡梯度的可能原因和有效对策的深入分析。

Video classification systems are vulnerable to adversarial attacks, which can create severe security problems in video verification. Current black-box attacks need a large number of queries to succeed, resulting in high computational overhead in the process of attack. On the other hand, attacks with restricted perturbations are ineffective against defenses such as denoising or adversarial training. In this paper, we focus on unrestricted perturbations and propose StyleFool, a black-box video adversarial attack via style transfer to fool the video classification system. StyleFool first utilizes color theme proximity to select the best style image, which helps avoid unnatural details in the stylized videos. Meanwhile, the target class confidence is additionally considered in targeted attacks to influence the output distribution of the classifier by moving the stylized video closer to or even across the decision boundary. A gradient-free method is then employed to further optimize the adversarial perturbations. We carry out extensive experiments to evaluate StyleFool on two standard datasets, UCF-101 and HMDB-51. The experimental results demonstrate that StyleFool outperforms the state-of-the-art adversarial attacks in terms of both the number of queries and the robustness against existing defenses. Moreover, 50% of the stylized videos in untargeted attacks do not need any query since they can already fool the video classification model. Furthermore, we evaluate the indistinguishability through a user study to show that the adversarial samples of StyleFool look imperceptible to human eyes, despite unrestricted perturbations.

Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve as an important surrogate to evaluate the robustness of deep learning models before they are deployed. However, most of existing adversarial attacks can only fool a black-box model with a low success rate. To address this issue, we propose a broad class of momentum-based iterative algorithms to boost adversarial attacks. By integrating the momentum term into the iterative process for attacks, our methods can stabilize update directions and escape from poor local maxima during the iterations, resulting in more transferable adversarial examples. To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks. We hope that the proposed methods will serve as a benchmark for evaluating the robustness of various deep models and defense methods. With this method, we won the first places in NIPS 2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack competitions.

尽管机器学习系统的效率和可扩展性，但最近的研究表明，许多分类方法，尤其是深神经网络（DNN），易受对抗的例子;即，仔细制作欺骗训练有素的分类模型的例子，同时无法区分从自然数据到人类。这使得在安全关键区域中应用DNN或相关方法可能不安全。由于这个问题是由Biggio等人确定的。 （2013）和Szegedy等人。（2014年），在这一领域已经完成了很多工作，包括开发攻击方法，以产生对抗的例子和防御技术的构建防范这些例子。本文旨在向统计界介绍这一主题及其最新发展，主要关注对抗性示例的产生和保护。在数值实验中使用的计算代码（在Python和R）公开可用于读者探讨调查的方法。本文希望提交人们将鼓励更多统计学人员在这种重要的令人兴奋的领域的产生和捍卫对抗的例子。

Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model's loss. We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss. The model thus learns to generate weak perturbations, rather than defend against strong ones. As a result, we find that adversarial training remains vulnerable to black-box attacks, where we transfer perturbations computed on undefended models, as well as to a powerful novel single-step attack that escapes the non-smooth vicinity of the input data via a small random step. We further introduce Ensemble Adversarial Training, a technique that augments training data with perturbations transferred from other models. On ImageNet, Ensemble Adversarial Training yields models with stronger robustness to blackbox attacks. In particular, our most robust model won the first round of the NIPS 2017 competition on Defenses against Adversarial Attacks (Kurakin et al., 2017c). However, subsequent work found that more elaborate black-box attacks could significantly enhance transferability and reduce the accuracy of our models.

Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from 95% to 0.5%.In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with 100% probability. Our attacks are tailored to three distance metrics used previously in the literature, and when compared to previous adversarial example generation algorithms, our attacks are often much more effective (and never worse). Furthermore, we propose using high-confidence adversarial examples in a simple transferability test we show can also be used to break defensive distillation. We hope our attacks will be used as a benchmark in future defense attempts to create neural networks that resist adversarial examples.

Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to adversarial examples. We find, however, that typical adaptive evaluations are incomplete. We demonstrate that thirteen defenses recently published at ICLR, ICML and NeurIPS-and which illustrate a diverse set of defense strategies-can be circumvented despite attempting to perform evaluations using adaptive attacks. While prior evaluation papers focused mainly on the end result-showing that a defense was ineffective-this paper focuses on laying out the methodology and the approach necessary to perform an adaptive attack. Some of our attack strategies are generalizable, but no single strategy would have been sufficient for all defenses. This underlines our key message that adaptive attacks cannot be automated and always require careful and appropriate tuning to a given defense. We hope that these analyses will serve as guidance on how to properly perform adaptive attacks against defenses to adversarial examples, and thus will allow the community to make further progress in building more robust models.

对抗攻击使他们的成功取得了“愚弄”DNN等，基于梯度的算法成为一个主流。基于线性假设[12]，在$ \ ell_ \ infty $约束下，在梯度上应用于渐变的$符号$操作是生成扰动的良好选择。然而，存在来自这种操作的副作用，因为它导致真实梯度与扰动之间的方向偏差。换句话说，当前方法包含真实梯度和实际噪声之间的间隙，这导致偏置和低效的攻击。因此，在理论上，基于泰勒膨胀，偏差地分析了$ \符号$，即快速梯度非符号法（FGNM）的校正。值得注意的是，FGNM是一般例程，它可以在基于梯度的攻击中无缝地更换传统的$符号$操作，以可忽略的额外计算成本。广泛的实验证明了我们方法的有效性。具体来说，我们的大多数和\ textBF {27.5 \％}平均突出了它们，平均而言。我们的匿名代码是公开可用的：\ url {https://git.io/mm -fgnm}。

Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial patches by either optimizing their perturbation values while fixing the pasting position or manipulating the position while fixing the patch's content. This reveals that the positions and perturbations are both important to the adversarial attack. For that, in this paper, we propose a novel method to simultaneously optimize the position and perturbation for an adversarial patch, and thus obtain a high attack success rate in the black-box setting. Technically, we regard the patch's position, the pre-designed hyper-parameters to determine the patch's perturbations as the variables, and utilize the reinforcement learning framework to simultaneously solve for the optimal solution based on the rewards obtained from the target model with a small number of queries. Extensive experiments are conducted on the Face Recognition (FR) task, and results on four representative FR models show that our method can significantly improve the attack success rate and query efficiency. Besides, experiments on the commercial FR service and physical environments confirm its practical application value. We also extend our method to the traffic sign recognition task to verify its generalization ability.