深度神经网络（DNN）在近年来，包括自动驾驶感知任务，包括自主驾驶感知任务的令人印象深刻。另一方面，目前的深神经网络很容易被对抗性攻击所欺骗。此漏洞提高了重要的问题，特别是在安全关键型应用中。因此，攻击和捍卫DNN的研究已经获得了很多覆盖范围。在这项工作中，横跨距离估计，语义分割，运动检测和对象检测，对详细的对抗攻击应用于各种多任务视觉感知深网络。实验考虑了针对目标和未定位案件的白色和黑色盒子攻击，同时攻击任务并检查所有其他效果，除了检查应用简单防御方法的效果。我们通过比较和讨论实验结果，提出见解和未来工作来结束本文。攻击的可视化可在https://youtu.be/6aixn90Budy上获得。

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.

基于深度神经网络（DNN）的智能信息（IOT）系统已被广泛部署在现实世界中。然而，发现DNNS易受对抗性示例的影响，这提高了人们对智能物联网系统的可靠性和安全性的担忧。测试和评估IOT系统的稳健性成为必要和必要。最近已经提出了各种攻击和策略，但效率问题仍未纠正。现有方法是计算地广泛或耗时，这在实践中不适用。在本文中，我们提出了一种称为攻击启发GaN（AI-GaN）的新框架，在有条件地产生对抗性实例。曾经接受过培训，可以有效地给予对抗扰动的输入图像和目标类。我们在白盒设置的不同数据集中应用AI-GaN，黑匣子设置和由最先进的防御保护的目标模型。通过广泛的实验，AI-GaN实现了高攻击成功率，优于现有方法，并显着降低了生成时间。此外，首次，AI-GaN成功地缩放到复杂的数据集。 Cifar-100和Imagenet，所有课程中的成功率约为90美元。

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。

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.

深度学习大大提高了单眼深度估计（MDE）的性能，这是完全基于视觉的自主驾驶（AD）系统（例如特斯拉和丰田）的关键组成部分。在这项工作中，我们对基于学习的MDE产生了攻击。特别是，我们使用基于优化的方法系统地生成隐形的物理对象贴片来攻击深度估计。我们通过面向对象的对抗设计，敏感的区域定位和自然风格的伪装来平衡攻击的隐身和有效性。使用现实世界的驾驶场景，我们评估了对并发MDE模型的攻击和AD的代表下游任务（即3D对象检测）。实验结果表明，我们的方法可以为不同的目标对象和模型生成隐形，有效和健壮的对抗贴片，并在物体检测中以1/1/的斑点检测到超过6米的平均深度估计误差和93％的攻击成功率（ASR）车辆后部9个。具有实际车辆的三个不同驾驶路线上的现场测试表明，在连续视频帧中，我们导致超过6米的平均深度估计误差，并将对象检测率从90.70％降低到5.16％。

考虑到整个时间领域的信息有助于改善自动驾驶中的环境感知。但是，到目前为止，尚未研究暂时融合的神经网络是否容易受到故意产生的扰动，即对抗性攻击，或者时间历史是否是对它们的固有防御。在这项工作中，我们研究了用于对象检测的时间特征网络是否容易受到通用对抗性攻击的影响。我们评估了两种类型的攻击：整个图像和本地界面贴片的不可察觉噪声。在这两种情况下，使用PGD以白盒方式生成扰动。我们的实验证实，即使攻击时间的一部分时间都足以欺骗网络。我们在视觉上评估生成的扰动，以了解攻击功能。为了增强鲁棒性，我们使用5-PGD应用对抗训练。我们在Kitti和Nuscenes数据集上进行的实验证明了通过K-PGD鲁棒化的模型能够承受研究的攻击，同时保持基于地图的性能与未破坏模型的攻击。

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.

通过对数据集的样本应用小而有意的最差情况扰动可以产生对抗性输入，这导致甚至最先进的深神经网络，以高信任输出不正确的答案。因此，开发了一些对抗防御技术来提高模型的安全性和稳健性，并避免它们被攻击。逐渐，攻击者和捍卫者之间的游戏类似的竞争，其中两个玩家都会试图在最大化自己的收益的同时互相反对发挥最佳策略。为了解决游戏，每个玩家都基于对对手的战略选择的预测来选择反对对手的最佳策略。在这项工作中，我们正处于防守方面，以申请防止攻击的游戏理论方法。我们使用两个随机化方法，随机初始化和随机激活修剪，以创造网络的多样性。此外，我们使用一种去噪技术，超级分辨率，通过在攻击前预处理图像来改善模型的鲁棒性。我们的实验结果表明，这三种方法可以有效提高深度学习神经网络的鲁棒性。

深度神经网络的图像分类容易受到对抗性扰动的影响。图像分类可以通过在输入图像中添加人造小且不可察觉的扰动来轻松愚弄。作为最有效的防御策略之一，提出了对抗性训练，以解决分类模型的脆弱性，其中创建了对抗性示例并在培训期间注入培训数据中。在过去的几年中，对分类模型的攻击和防御进行了深入研究。语义细分作为分类的扩展，最近也受到了极大的关注。最近的工作表明，需要大量的攻击迭代来创建有效的对抗性示例来欺骗分割模型。该观察结果既可以使鲁棒性评估和对分割模型的对抗性培训具有挑战性。在这项工作中，我们提出了一种称为SEGPGD的有效有效的分割攻击方法。此外，我们提供了收敛分析，以表明在相同数量的攻击迭代下，提出的SEGPGD可以创建比PGD更有效的对抗示例。此外，我们建议将SEGPGD应用于分割对抗训练的基础攻击方法。由于SEGPGD可以创建更有效的对抗性示例，因此使用SEGPGD的对抗训练可以提高分割模型的鲁棒性。我们的建议还通过对流行分割模型体系结构和标准分段数据集进行了验证。

大多数对抗攻击防御方法依赖于混淆渐变。这些方法在捍卫基于梯度的攻击方面是成功的;然而，它们容易被攻击绕过，该攻击不使用梯度或近似近似和使用校正梯度的攻击。不存在不存在诸如对抗培训等梯度的防御，但这些方法通常对诸如其幅度的攻击进行假设。我们提出了一种分类模型，该模型不会混淆梯度，并且通过施工而强大而不承担任何关于攻击的知识。我们的方法将分类作为优化问题，我们“反转”在不受干扰的自然图像上培训的条件发电机，以找到生成最接近查询图像的类。我们假设潜在的脆性抗逆性攻击源是前馈分类器的高度低维性质，其允许对手发现输入空间中的小扰动，从而导致输出空间的大变化。另一方面，生成模型通常是低到高维的映射。虽然该方法与防御GaN相关，但在我们的模型中使用条件生成模型和反演而不是前馈分类是临界差异。与Defense-GaN不同，它被证明生成了容易规避的混淆渐变，我们表明我们的方法不会混淆梯度。我们展示了我们的模型对黑箱攻击的极其强劲，并与自然训练的前馈分类器相比，对白盒攻击的鲁棒性提高。

In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks. Defense-GAN is trained to model the distribution of unperturbed images. At inference time, it finds a close output to a given image which does not contain the adversarial changes. This output is then fed to the classifier. Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure. It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples. We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies. Our code has been made publicly available at https://github.com/kabkabm/defensegan.

Deep learning-based 3D object detectors have made significant progress in recent years and have been deployed in a wide range of applications. It is crucial to understand the robustness of detectors against adversarial attacks when employing detectors in security-critical applications. In this paper, we make the first attempt to conduct a thorough evaluation and analysis of the robustness of 3D detectors under adversarial attacks. Specifically, we first extend three kinds of adversarial attacks to the 3D object detection task to benchmark the robustness of state-of-the-art 3D object detectors against attacks on KITTI and Waymo datasets, subsequently followed by the analysis of the relationship between robustness and properties of detectors. Then, we explore the transferability of cross-model, cross-task, and cross-data attacks. We finally conduct comprehensive experiments of defense for 3D detectors, demonstrating that simple transformations like flipping are of little help in improving robustness when the strategy of transformation imposed on input point cloud data is exposed to attackers. Our findings will facilitate investigations in understanding and defending the adversarial attacks against 3D object detectors to advance this field.

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.

虽然近年来，在2D图像领域的攻击和防御中，许多努力已经探讨了3D模型的脆弱性。现有的3D攻击者通常在点云上执行点明智的扰动，从而导致变形的结构或异常值，这很容易被人类察觉。此外，它们的对抗示例是在白盒设置下产生的，当转移到攻击远程黑匣子型号时经常遭受低成功率。在本文中，我们通过提出一种新的难以察觉的转移攻击（ITA）：1）难以察觉的3D点云攻击来自两个新的和具有挑战性的观点：1）难以察觉：沿着邻域表面的正常向量限制每个点的扰动方向，导致产生具有类似几何特性的示例，从而增强了难以察觉。 2）可转移性：我们开发了一个对抗性转变模型，以产生最有害的扭曲，并强制实施对抗性示例来抵抗它，从而提高其对未知黑匣子型号的可转移性。此外，我们建议通过学习更辨别的点云表示来培训更强大的黑盒3D模型来防御此类ITA攻击。广泛的评估表明，我们的ITA攻击比最先进的人更令人无法察觉和可转让，并验证我们的国防战略的优势。

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.

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。

Video compression plays a crucial role in video streaming and classification systems by maximizing the end-user quality of experience (QoE) at a given bandwidth budget. In this paper, we conduct the first systematic study for adversarial attacks on deep learning-based video compression and downstream classification systems. Our attack framework, dubbed RoVISQ, manipulates the Rate-Distortion ($\textit{R}$-$\textit{D}$) relationship of a video compression model to achieve one or both of the following goals: (1) increasing the network bandwidth, (2) degrading the video quality for end-users. We further devise new objectives for targeted and untargeted attacks to a downstream video classification service. Finally, we design an input-invariant perturbation that universally disrupts video compression and classification systems in real time. Unlike previously proposed attacks on video classification, our adversarial perturbations are the first to withstand compression. We empirically show the resilience of RoVISQ attacks against various defenses, i.e., adversarial training, video denoising, and JPEG compression. Our extensive experimental results on various video datasets show RoVISQ attacks deteriorate peak signal-to-noise ratio by up to 5.6dB and the bit-rate by up to $\sim$ 2.4$\times$ while achieving over 90$\%$ attack success rate on a downstream classifier. Our user study further demonstrates the effect of RoVISQ attacks on users' QoE.

我们提出了一种对光学流动的语义靶向对抗攻击的新方法。在这种攻击中，目标是损坏特定对象类别或实例的流预测。通常，攻击者寻求隐藏输入中的对抗扰动。但是，输出的快速扫描显示攻击。相比之下，我们的方法有助于隐藏输出中的攻击者。由于促进偏移目标一致性的正常化术语，我们实现这一点。我们在领先的光学流模型上进行广泛的测试，以展示我们在白盒和黑匣子设置中的方法的好处。此外，我们展示了我们对依赖于光学流量的后续任务的攻击的有效性。