基于深的神经网络（DNNS）基于合成孔径雷达（SAR）自动靶标识别（ATR）系统已显示出非常容易受到故意设计但几乎无法察觉的对抗扰动的影响，但是当添加到靶向物体中时，DNN推断可能会偏差。在将DNN应用于高级SAR ATR应用时，这会导致严重的安全问题。因此，增强DNN的对抗性鲁棒性对于对现代现实世界中的SAR ATR系统实施DNN至关重要。本文旨在构建更健壮的DNN基于DNN的SAR ATR模型，探讨了SAR成像过程的领域知识，并提出了一种新型的散射模型引导的对抗攻击（SMGAA）算法，该算法可以以电磁散射响应的形式产生对抗性扰动（称为对抗散射器） ）。提出的SMGAA由两个部分组成：1）参数散射模型和相应的成像方法以及2）基于自定义的基于梯度的优化算法。首先，我们介绍了有效的归因散射中心模型（ASCM）和一种通用成像方法，以描述SAR成像过程中典型几何结构的散射行为。通过进一步制定几种策略来考虑SAR目标图像的领域知识并放松贪婪的搜索程序，建议的方法不需要经过审慎的态度，但是可以有效地找到有效的ASCM参数来欺骗SAR分类器并促进SAR分类器并促进强大的模型训练。对MSTAR数据集的全面评估表明，SMGAA产生的对抗散射器对SAR处理链中的扰动和转换比当前研究的攻击更为强大，并且有效地构建了针对恶意散射器的防御模型。

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。

已知深度神经网络（DNN）容易受到用不可察觉的扰动制作的对抗性示例的影响，即，输入图像的微小变化会引起错误的分类，从而威胁着基于深度学习的部署系统的可靠性。经常采用对抗训练（AT）来通过训练损坏和干净的数据的混合物来提高DNN的鲁棒性。但是，大多数基于AT的方法在处理\ textit {转移的对抗示例}方面是无效的，这些方法是生成以欺骗各种防御模型的生成的，因此无法满足现实情况下提出的概括要求。此外，对抗性训练一般的国防模型不能对具有扰动的输入产生可解释的预测，而不同的领域专家则需要一个高度可解释的强大模型才能了解DNN的行为。在这项工作中，我们提出了一种基于Jacobian规范和选择性输入梯度正则化（J-SIGR）的方法，该方法通过Jacobian归一化提出了线性化的鲁棒性，还将基于扰动的显着性图正规化，以模仿模型的可解释预测。因此，我们既可以提高DNN的防御能力和高解释性。最后，我们评估了跨不同体系结构的方法，以针对强大的对抗性攻击。实验表明，提出的J-Sigr赋予了针对转移的对抗攻击的鲁棒性，我们还表明，来自神经网络的预测易于解释。

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。

基于深度学习的图像识别系统已广泛部署在当今世界的移动设备上。然而，在最近的研究中，深入学习模型被证明易受对抗的例子。一种逆势例的一个变种，称为对抗性补丁，由于其强烈的攻击能力而引起了研究人员的注意。虽然对抗性补丁实现了高攻击成功率，但由于补丁和原始图像之间的视觉不一致，它们很容易被检测到。此外，它通常需要对文献中的对抗斑块产生的大量数据，这是计算昂贵且耗时的。为了解决这些挑战，我们提出一种方法来产生具有一个单一图像的不起眼的对抗性斑块。在我们的方法中，我们首先通过利用多尺度发生器和鉴别器来决定基于受害者模型的感知敏感性的补丁位置，然后以粗糙的方式产生对抗性斑块。鼓励修补程序与具有对抗性训练的背景图像一致，同时保留强烈的攻击能力。我们的方法显示了白盒设置中的强烈攻击能力以及通过对具有不同架构和培训方法的各种型号的广泛实验，通过广泛的实验进行黑盒设置的优异转移性。与其他对抗贴片相比，我们的对抗斑块具有最大忽略的风险，并且可以避免人类观察，这是由显着性图和用户评估结果的插图支持的人类观察。最后，我们表明我们的对抗性补丁可以应用于物理世界。

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.

Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth observation (EO) missions, from low-level vision tasks like super-resolution, denoising, and inpainting, to high-level vision tasks like scene classification, object detection, and semantic segmentation. While AI techniques enable researchers to observe and understand the Earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety-critical. This paper reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning, uncertainty, and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors' knowledge, this paper is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the paper to move this vibrant field of research forward.

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.

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.

To assess the vulnerability of deep learning in the physical world, recent works introduce adversarial patches and apply them on different tasks. In this paper, we propose another kind of adversarial patch: the Meaningful Adversarial Sticker, a physically feasible and stealthy attack method by using real stickers existing in our life. Unlike the previous adversarial patches by designing perturbations, our method manipulates the sticker's pasting position and rotation angle on the objects to perform physical attacks. Because the position and rotation angle are less affected by the printing loss and color distortion, adversarial stickers can keep good attacking performance in the physical world. Besides, to make adversarial stickers more practical in real scenes, we conduct attacks in the black-box setting with the limited information rather than the white-box setting with all the details of threat models. To effectively solve for the sticker's parameters, we design the Region based Heuristic Differential Evolution Algorithm, which utilizes the new-found regional aggregation of effective solutions and the adaptive adjustment strategy of the evaluation criteria. Our method is comprehensively verified in the face recognition and then extended to the image retrieval and traffic sign recognition. Extensive experiments show the proposed method is effective and efficient in complex physical conditions and has a good generalization for different tasks.

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。

Deep learning methods have gained increased attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge$^{+}$, that deceive both the target deep learning model and the coupled interpretation model. We assess the effectiveness of proposed attacks against two deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models. Our experiments include the attack implementation using various attack frameworks. We also explore the potential countermeasures against such attacks. Our analysis shows the effectiveness of our attacks in terms of deceiving the deep learning models and their interpreters, and highlights insights to improve and circumvent the attacks.

深度神经网络已被证明容易受到对抗图像的影响。常规攻击努力争取严格限制扰动的不可分割的对抗图像。最近，研究人员已采取行动探索可区分但非奇异的对抗图像，并证明色彩转化攻击是有效的。在这项工作中，我们提出了对抗颜色过滤器（ADVCF），这是一种新颖的颜色转换攻击，在简单颜色滤波器的参数空间中通过梯度信息进行了优化。特别是，明确指定了我们的颜色滤波器空间，以便从攻击和防御角度来对对抗性色转换进行系统的鲁棒性分析。相反，由于缺乏这种明确的空间，现有的颜色转换攻击并不能为系统分析提供机会。我们通过用户研究进一步进行了对成功率和图像可接受性的不同颜色转化攻击之间的广泛比较。其他结果为在另外三个视觉任务中针对ADVCF的模型鲁棒性提供了有趣的新见解。我们还强调了ADVCF的人类解剖性，该advcf在实际使用方案中有希望，并显示出比对图像可接受性和效率的最新人解释的色彩转化攻击的优越性。

深度学习的进步使得广泛的有希望的应用程序。然而，这些系统容易受到对抗机器学习（AML）攻击的影响;对他们的意见的离前事实制作的扰动可能导致他们错误分类。若干最先进的对抗性攻击已经证明他们可以可靠地欺骗分类器，使这些攻击成为一个重大威胁。对抗性攻击生成算法主要侧重于创建成功的例子，同时控制噪声幅度和分布，使检测更加困难。这些攻击的潜在假设是脱机产生的对抗噪声，使其执行时间是次要考虑因素。然而，最近，攻击者机会自由地产生对抗性示例的立即对抗攻击已经可能。本文介绍了一个新问题：我们如何在实时约束下产生对抗性噪音，以支持这种实时对抗攻击？了解这一问题提高了我们对这些攻击对实时系统构成的威胁的理解，并为未来防御提供安全评估基准。因此，我们首先进行对抗生成算法的运行时间分析。普遍攻击脱机产生一般攻击，没有在线开销，并且可以应用于任何输入;然而，由于其一般性，他们的成功率是有限的。相比之下，在特定输入上工作的在线算法是计算昂贵的，使它们不适合在时间约束下的操作。因此，我们提出房间，一种新型实时在线脱机攻击施工模型，其中离线组件用于预热在线算法，使得可以在时间限制下产生高度成功的攻击。

深度神经网络在许多重要的遥感任务中取得了巨大的成功。然而，不应忽略它们对对抗性例子的脆弱性。在这项研究中，我们第一次系统地在遥感数据中系统地分析了普遍的对抗示例，而没有受害者模型的任何知识。具体而言，我们提出了一种新型的黑盒对抗攻击方法，即混合攻击及其简单的变体混合尺寸攻击，用于遥感数据。提出方法的关键思想是通过攻击给定替代模型的浅层层中的特征来找到不同网络之间的共同漏洞。尽管它们很简单，但提出的方法仍可以生成可转移的对抗性示例，这些示例欺骗了场景分类和语义分割任务的大多数最新深层神经网络，并具有很高的成功率。我们进一步在名为AUAE-RS的数据集中提供了生成的通用对抗示例，该数据集是第一个在遥感字段中提供黑色框对面样本的数据集。我们希望阿联酋可以用作基准，以帮助研究人员设计具有对遥感领域对抗攻击的强烈抵抗力的深神经网络。代码和阿联酋-RS数据集可在线获得（https://github.com/yonghaoxu/uae-rs）。

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。

强有力的对手例子是评估和增强深神经网络鲁棒性的关键。流行的对抗性攻击算法使用梯度上升最大化非cave损失函数。但是，每种攻击的性能通常对由于信息不足（仅一个输入示例，几乎没有白色盒子源模型和未知的防御策略）而敏感。因此，精心设计的对抗性示例容易过度拟合源模型，从而将其转移性限制在身份不明的架构上。在本文中，我们提出了多种渐近正态分布攻击（Multianda），这是一种新颖的方法，可以明确表征来自学习分布的对抗性扰动。具体而言，我们通过利用随机梯度上升（SGA）的渐近正态性能（SGA）的优势来近似于扰动，然后将整体策略应用于此过程，以估算高斯混合模型，以更好地探索潜在的优化空间。从学习分布中绘制扰动使我们能够为每个输入生成任何数量的对抗示例。近似后验实质上描述了SGA迭代的固定分布，该分布捕获了局部最佳距离周围的几何信息。因此，从分布中得出的样品可靠地保持转移性。我们提出的方法通过对七个正常训练和七个防御模型进行广泛的实验，超过了对具有或没有防御的深度学习模型的九个最先进的黑盒攻击。

深度神经网络容易受到来自对抗性投入的攻击，并且最近，特洛伊木马误解或劫持模型的决定。我们通过探索有界抗逆性示例空间和生成的对抗网络内的自然输入空间来揭示有界面的对抗性实例 - 通用自然主义侵害贴片的兴趣类 - 我们呼叫TNT。现在，一个对手可以用一个自然主义的补丁来手臂自己，不太恶意，身体上可实现，高效 - 实现高攻击成功率和普遍性。 TNT是普遍的，因为在场景中的TNT中捕获的任何输入图像都将：i）误导网络（未确定的攻击）;或ii）迫使网络进行恶意决定（有针对性的攻击）。现在，有趣的是，一个对抗性补丁攻击者有可能发挥更大的控制水平 - 选择一个独立，自然的贴片的能力，与被限制为嘈杂的扰动的触发器 - 到目前为止只有可能与特洛伊木马攻击方法有可能干扰模型建设过程，以嵌入风险发现的后门;但是，仍然意识到在物理世界中部署的补丁。通过对大型视觉分类任务的广泛实验，想象成在其整个验证集50,000张图像中进行评估，我们展示了TNT的现实威胁和攻击的稳健性。我们展示了攻击的概括，以创建比现有最先进的方法实现更高攻击成功率的补丁。我们的结果表明，攻击对不同的视觉分类任务（CIFAR-10，GTSRB，PUBFIG）和多个最先进的深神经网络，如WieredEnet50，Inception-V3和VGG-16。

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.