The authors thank Nicholas Carlini (UC Berkeley) and Dimitris Tsipras (MIT) for feedback to improve the survey quality. We also acknowledge X. Huang (Uni. Liverpool), K. R. Reddy (IISC), E. Valle (UNICAMP), Y. Yoo (CLAIR) and others for providing pointers to make the survey more comprehensive.

With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks have been recently found vulnerable to well-designed input samples, called adversarial examples. Adversarial perturbations are imperceptible to human but can easily fool deep neural networks in the testing/deploying stage. The vulnerability to adversarial examples becomes one of the major risks for applying deep neural networks in safety-critical environments. Therefore, attacks and defenses on adversarial examples draw great attention. In this paper, we review recent findings on adversarial examples for deep neural networks, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods. Under the taxonomy, applications for adversarial examples are investigated. We further elaborate on countermeasures for adversarial examples. In addition, three major challenges in adversarial examples and the potential solutions are discussed.

对抗性实例的有趣现象引起了机器学习中的显着关注，对社区可能更令人惊讶的是存在普遍对抗扰动（UAPS），即欺骗目标DNN的单一扰动。随着对深层分类器的关注，本调查总结了最近普遍对抗攻击的进展，讨论了攻击和防御方的挑战，以及uap存在的原因。我们的目标是将此工作扩展为动态调查，该调查将定期更新其内容，以遵循关于在广泛的域中的UAP或通用攻击的新作品，例如图像，音频，视频，文本等。将讨论相关更新：https://bit.ly/2sbqlgg。我们欢迎未来的作者在该领域的作品，联系我们，包括您的新发现。

Although deep neural networks (DNNs) have achieved great success in many tasks, they can often be fooled by adversarial examples that are generated by adding small but purposeful distortions to natural examples. Previous studies to defend against adversarial examples mostly focused on refining the DNN models, but have either shown limited success or required expensive computation. We propose a new strategy, feature squeezing, that can be used to harden DNN models by detecting adversarial examples. Feature squeezing reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample. By comparing a DNN model's prediction on the original input with that on squeezed inputs, feature squeezing detects adversarial examples with high accuracy and few false positives.This paper explores two feature squeezing methods: reducing the color bit depth of each pixel and spatial smoothing. These simple strategies are inexpensive and complementary to other defenses, and can be combined in a joint detection framework to achieve high detection rates against state-of-the-art attacks.

深度学习（DL）在许多与人类相关的任务中表现出巨大的成功，这导致其在许多计算机视觉的基础应用中采用，例如安全监控系统，自治车辆和医疗保健。一旦他们拥有能力克服安全关键挑战，这种安全关键型应用程序必须绘制他们的成功部署之路。在这些挑战中，防止或/和检测对抗性实例（AES）。对手可以仔细制作小型，通常是难以察觉的，称为扰动的噪声被添加到清洁图像中以产生AE。 AE的目的是愚弄DL模型，使其成为DL应用的潜在风险。在文献中提出了许多测试时间逃避攻击和对策，即防御或检测方法。此外，还发布了很少的评论和调查，理论上展示了威胁的分类和对策方法，几乎​​没有焦点检测方法。在本文中，我们专注于图像分类任务，并试图为神经网络分类器进行测试时间逃避攻击检测方法的调查。对此类方法的详细讨论提供了在四个数据集的不同场景下的八个最先进的探测器的实验结果。我们还为这一研究方向提供了潜在的挑战和未来的观点。

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。

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。

在本讨论文件中，我们调查了有关机器学习模型鲁棒性的最新研究。随着学习算法在数据驱动的控制系统中越来越流行，必须确保它们对数据不确定性的稳健性，以维持可靠的安全至关重要的操作。我们首先回顾了这种鲁棒性的共同形式主义，然后继续讨论训练健壮的机器学习模型的流行和最新技术，以及可证明这种鲁棒性的方法。从强大的机器学习的这种统一中，我们识别并讨论了该地区未来研究的迫切方向。

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.

Although Deep Neural Networks (DNNs) have achieved impressive results in computer vision, their exposed vulnerability to adversarial attacks remains a serious concern. A series of works has shown that by adding elaborate perturbations to images, DNNs could have catastrophic degradation in performance metrics. And this phenomenon does not only exist in the digital space but also in the physical space. Therefore, estimating the security of these DNNs-based systems is critical for safely deploying them in the real world, especially for security-critical applications, e.g., autonomous cars, video surveillance, and medical diagnosis. In this paper, we focus on physical adversarial attacks and provide a comprehensive survey of over 150 existing papers. We first clarify the concept of the physical adversarial attack and analyze its characteristics. Then, we define the adversarial medium, essential to perform attacks in the physical world. Next, we present the physical adversarial attack methods in task order: classification, detection, and re-identification, and introduce their performance in solving the trilemma: effectiveness, stealthiness, and robustness. In the end, we discuss the current challenges and potential future directions.

在过去的十年中，深度学习急剧改变了传统的手工艺特征方式，具有强大的功能学习能力，从而极大地改善了传统任务。然而，最近已经证明了深层神经网络容易受到对抗性例子的影响，这种恶意样本由小型设计的噪音制作，误导了DNNs做出错误的决定，同时仍然对人类无法察觉。对抗性示例可以分为数字对抗攻击和物理对抗攻击。数字对抗攻击主要是在实验室环境中进行的，重点是改善对抗性攻击算法的性能。相比之下，物理对抗性攻击集中于攻击物理世界部署的DNN系统，这是由于复杂的物理环境（即亮度，遮挡等），这是一项更具挑战性的任务。尽管数字对抗和物理对抗性示例之间的差异很小，但物理对抗示例具有特定的设计，可以克服复杂的物理环境的效果。在本文中，我们回顾了基于DNN的计算机视觉任务任务中的物理对抗攻击的开发，包括图像识别任务，对象检测任务和语义细分。为了完整的算法演化，我们将简要介绍不涉及身体对抗性攻击的作品。我们首先提出一个分类方案，以总结当前的物理对抗攻击。然后讨论现有的物理对抗攻击的优势和缺点，并专注于用于维持对抗性的技术，当应用于物理环境中时。最后，我们指出要解决的当前身体对抗攻击的问题并提供有前途的研究方向。

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

深度学习（DL）系统的安全性是一个极为重要的研究领域，因为它们正在部署在多个应用程序中，因为它们不断改善，以解决具有挑战性的任务。尽管有压倒性的承诺，但深度学习系统容易受到制作的对抗性例子的影响，这可能是人眼无法察觉的，但可能会导致模型错误分类。对基于整体技术的对抗性扰动的保护已被证明很容易受到更强大的对手的影响，或者证明缺乏端到端评估。在本文中，我们试图开发一种新的基于整体的解决方案，该解决方案构建具有不同决策边界的防御者模型相对于原始模型。通过（1）通过一种称为拆分和剃须的方法转换输入的分类器的合奏，以及（2）通过一种称为对比度功能的方法限制重要特征，显示出相对于相对于不同的梯度对抗性攻击，这减少了将对抗性示例从原始示例转移到针对同一类的防御者模型的机会。我们使用标准图像分类数据集（即MNIST，CIFAR-10和CIFAR-100）进行了广泛的实验，以实现最新的对抗攻击，以证明基于合奏的防御的鲁棒性。我们还在存在更强大的对手的情况下评估稳健性，该对手同时靶向合奏中的所有模型。已经提供了整体假阳性和误报的结果，以估计提出的方法的总体性能。

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.

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.

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.

由于它们在各个域中的大量成功，深入的学习技术越来越多地用于设计网络入侵检测解决方案，该解决方案检测和减轻具有高精度检测速率和最小特征工程的未知和已知的攻击。但是，已经发现，深度学习模型容易受到可以误导模型的数据实例，以使所谓的分类决策不正确（对抗示例）。此类漏洞允许攻击者通过向恶意流量添加小的狡猾扰动来逃避检测并扰乱系统的关键功能。在计算机视觉域中广泛研究了深度对抗学习的问题;但是，它仍然是网络安全应用中的开放研究领域。因此，本调查探讨了在网络入侵检测领域采用对抗机器学习的不同方面的研究，以便为潜在解决方案提供方向。首先，调查研究基于它们对产生对抗性实例的贡献来分类，评估ML的NID对逆势示例的鲁棒性，并捍卫这些模型的这种攻击。其次，我们突出了调查研究中确定的特征。此外，我们讨论了现有的通用对抗攻击对NIDS领域的适用性，启动拟议攻击在现实世界方案中的可行性以及现有缓解解决方案的局限性。

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.

This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. We motivate adversarial risk as an objective for achieving models robust to worst-case inputs. We then frame commonly used attacks and evaluation metrics as defining a tractable surrogate objective to the true adversarial risk. This suggests that models may optimize this surrogate rather than the true adversarial risk. We formalize this notion as obscurity to an adversary, and develop tools and heuristics for identifying obscured models and designing transparent models. We demonstrate that this is a significant problem in practice by repurposing gradient-free optimization techniques into adversarial attacks, which we use to decrease the accuracy of several recently proposed defenses to near zero. Our hope is that our formulations and results will help researchers to develop more powerful defenses.

对卷积神经网络（CNN）的对抗性攻击的存在质疑这种模型对严重应用的适合度。攻击操纵输入图像，使得错误分类是在对人类观察者看上去正常的同时唤起的 - 因此它们不容易被检测到。在不同的上下文中，CNN隐藏层的反向传播激活（对给定输入的“特征响应”）有助于可视化人类“调试器” CNN“在计算其输出时对CNN”的看法。在这项工作中，我们提出了一种新颖的检测方法，以防止攻击。我们通过在特征响应中跟踪对抗扰动来做到这一点，从而可以使用平均局部空间熵自动检测。该方法不会改变原始的网络体系结构，并且完全可以解释。实验证实了我们对在Imagenet训练的大规模模型的最新攻击方法的有效性。