深度神经网络（DNN）已广泛采用健康风险预测，以提供医疗保健诊断和治疗。为了评估其稳健性，现有研究在型号参数可访问的白色/灰度箱设置中进行对抗性攻击。然而，即使大多数现实世界的型号训练私有数据并在云上作为黑匣子服务发布，也是更现实的黑盒对抗性攻击。为了填补这一差距，我们提出了针对Medattacker的健康风险预测模型的第一个黑匣子对抗攻击方法来调查他们的脆弱性。 MedAttacker通过两个步骤解决了EHR数据所带来的挑战：层次定位选择，它选择强化学习（RL）框架中的攻击位置并替换替代替代基于分数的原则。特别是，通过考虑EHR中的时间上下文，它通过使用每次访问的贡献分数和每个代码的显着分数来初始化其RL位置选择策略，这可以与决定性变化决定的确定性替代选择过程很好地集成。在实验中，Medattacker始终如一地实现了最高的平均成功率，并且在某些情况下攻击了在多次真实数据集中的黑匣子环境中的三个高级健康风险预测模型时，最近的白盒EHR对抗攻击技术甚至优于最近的白盒EHR对抗性攻击技术。此外，基于实验结果，我们包括讨论捍卫EHR对抗性攻击。

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.

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.

尽管在许多机器学习任务方面取得了巨大成功，但深度神经网络仍然易于对抗对抗样本。虽然基于梯度的对抗攻击方法在计算机视野领域探索，但由于文本的离散性质，直接应用于自然语言处理中，这是不切实际的。为了弥合这一差距，我们提出了一般框架，以适应现有的基于梯度的方法来制作文本对抗性样本。在该框架中，将基于梯度的连续扰动添加到嵌入层中，并在前向传播过程中被放大。然后用掩模语言模型头解码最终的扰动潜在表示以获得潜在的对抗性样本。在本文中，我们将我们的框架与\ textbf {t} Extual \ TextBF {P} ROJECTED \ TextBF {G} Radient \ TextBF {D} excent（\ TextBF {TPGD}）进行ronject \ textbf {p}。我们通过在三个基准数据集上执行转移黑匣子攻击来评估我们的框架来评估我们的框架。实验结果表明，与强基线方法相比，我们的方法达到了更好的性能，并产生更精细和语法的对抗性样本。所有代码和数据都将公开。

Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much high dimension, which brings huge computational costs when generating adversarial videos. This is especially serious for the query-based black-box attacks where gradient estimation for the threat models is usually utilized, and high dimensions will lead to a large number of queries. To mitigate this issue, we propose to simultaneously eliminate the temporal and spatial redundancy within the video to achieve an effective and efficient gradient estimation on the reduced searching space, and thus query number could decrease. To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video. AstFocus attack is based on the cooperative Multi-Agent Reinforcement Learning (MARL) framework. One agent is responsible for selecting key frames, and another agent is responsible for selecting key regions. These two agents are jointly trained by the common rewards received from the black-box threat models to perform a cooperative prediction. By continuously querying, the reduced searching space composed of key frames and key regions is becoming precise, and the whole query number becomes less than that on the original video. Extensive experiments on four mainstream video recognition models and three widely used action recognition datasets demonstrate that the proposed AstFocus attack outperforms the SOTA methods, which is prevenient in fooling rate, query number, time, and perturbation magnitude at the same.

最近的研究表明，深神经网络（DNN）易受对抗的对抗性斑块，这引入了对输入的可察觉而且局部化的变化。尽管如此，现有的方法都集中在图像上产生对抗性补丁，视频中的对应于视频的探索。与图像相比，攻击视频更具挑战性，因为它不仅需要考虑空间线索，而且需要考虑时间线索。为了缩短这种差距，我们在本文中介绍了一种新的对抗性攻击，子弹屏幕评论（BSC）攻击，攻击了BSC的视频识别模型。具体地，通过增强学习（RL）框架产生对抗性BSC，其中环境被设置为目标模型，并且代理商扮演选择每个BSC的位置和透明度的作用。通过不断查询目标模型和接收反馈，代理程序逐渐调整其选择策略，以实现具有非重叠BSC的高鬼速。由于BSC可以被视为一种有意义的补丁，将它添加到清洁视频不会影响人们对视频内容的理解，也不会引起人们的怀疑。我们进行广泛的实验，以验证所提出的方法的有效性。在UCF-101和HMDB-51数据集中，我们的BSC攻击方法可以在攻击三个主流视频识别模型时达到约90 \％的愚蠢速率，同时仅在视频中封闭\无文无线8 \％区域。我们的代码可在https://github.com/kay -ck/bsc-attack获得。

愚弄深度神经网络（DNN）与黑匣子优化已成为一种流行的对抗攻击方式，因为DNN的结构先验知识始终是未知的。尽管如此，最近的黑匣子对抗性攻击可能会努力平衡其在解决高分辨率图像中产生的对抗性示例（AES）的攻击能力和视觉质量。在本文中，我们基于大规模的多目标进化优化，提出了一种关注引导的黑盒逆势攻击，称为LMOA。通过考虑图像的空间语义信息，我们首先利用注意图来确定扰动像素。而不是攻击整个图像，减少了具有注意机制的扰动像素可以有助于避免维度的臭名臭氧，从而提高攻击性能。其次，采用大规模的多目标进化算法在突出区域中遍历降低的像素。从其特征中受益，所产生的AES有可能在人类视力不可知的同时愚弄目标DNN。广泛的实验结果已经验证了所提出的LMOA在ImageNet数据集中的有效性。更重要的是，与现有的黑匣子对抗性攻击相比，产生具有更好的视觉质量的高分辨率AE更具竞争力。

Robustness evaluation against adversarial examples has become increasingly important to unveil the trustworthiness of the prevailing deep models in natural language processing (NLP). However, in contrast to the computer vision domain where the first-order projected gradient descent (PGD) is used as the benchmark approach to generate adversarial examples for robustness evaluation, there lacks a principled first-order gradient-based robustness evaluation framework in NLP. The emerging optimization challenges lie in 1) the discrete nature of textual inputs together with the strong coupling between the perturbation location and the actual content, and 2) the additional constraint that the perturbed text should be fluent and achieve a low perplexity under a language model. These challenges make the development of PGD-like NLP attacks difficult. To bridge the gap, we propose TextGrad, a new attack generator using gradient-driven optimization, supporting high-accuracy and high-quality assessment of adversarial robustness in NLP. Specifically, we address the aforementioned challenges in a unified optimization framework. And we develop an effective convex relaxation method to co-optimize the continuously-relaxed site selection and perturbation variables and leverage an effective sampling method to establish an accurate mapping from the continuous optimization variables to the discrete textual perturbations. Moreover, as a first-order attack generation method, TextGrad can be baked into adversarial training to further improve the robustness of NLP models. Extensive experiments are provided to demonstrate the effectiveness of TextGrad not only in attack generation for robustness evaluation but also in adversarial defense.

对抗性攻击提供了研究深层学习模式的稳健性的好方法。基于转移的黑盒攻击中的一种方法利用了几种图像变换操作来提高对逆势示例的可转换性，这是有效的，但不能考虑输入图像的特定特征。在这项工作中，我们提出了一种新颖的架构，称为自适应图像转换学习者（AIT1），其将不同的图像变换操作结合到统一的框架中，以进一步提高对抗性示例的可转移性。与现有工作中使用的固定组合变换不同，我们精心设计的转换学习者自适应地选择特定于输入图像的图像变换最有效的组合。关于Imagenet的广泛实验表明，我们的方法在各种设置下显着提高了正常培训的模型和防御模型的攻击成功率。

对抗性攻击可以迫使基于CNN的模型通过巧妙地操纵人类侵犯的输入来产生不正确的输出。探索这种扰动可以帮助我们更深入地了解神经网络的脆弱性，并为反对杂项对手提供深入学习的鲁棒性。尽管大量研究着重于图像，音频和NLP的鲁棒性，但仍缺乏视觉对象跟踪的对抗示例（尤其是以黑盒方式）的作品。在本文中，我们提出了一种新颖的对抗性攻击方法，以在黑色框设置下为单个对象跟踪产生噪音，其中仅在跟踪序列的初始框架上添加了扰动，从整个视频剪辑的角度来看，这很难注意到这一点。具体而言，我们将算法分为三个组件，并利用加固学习，以精确地定位重要的框架贴片，同时减少不必要的计算查询开销。与现有技术相比，我们的方法需要在视频的初始化框架上进行更少的查询，以操纵竞争性甚至更好的攻击性能。我们在长期和短期数据集中测试我们的算法，包括OTB100，DOCT2018，UAV123和LASOT。广泛的实验证明了我们方法对三种主流类型的跟踪器类型的有效性：歧视，基于暹罗和强化学习的跟踪器。

深度神经网络在解决各种现实世界任务中具有广泛的应用，并在计算机视觉，图像分类和自然语言处理等域中实现了令人满意的结果。同时，神经网络的安全性和稳健性成为必要的，因为不同的研究表明了神经网络的脆弱方面。在点的情况下，在自然语言处理任务中，神经网络可以由秘密修改的文本欺骗，这与原始文本具有高相似性。根据以前的研究，大多数研究都集中在图像领域;与图像逆势攻击不同，文本以离散序列表示，传统的图像攻击方法不适用于NLP字段。在本文中，我们提出了一个单词级NLP情绪分类器攻击模型，包括一种基于自我关注机制的词选择方法和用于Word替换的贪婪搜索算法。我们通过在IMDB数据集中攻击GRU和1D-CNN受害者模型进行攻击模型进行实验。实验结果表明，我们的模型达到了更高的攻击成功率，并且比以前的方法更有效，因为由于有效的单词选择算法，并且最小化了单词替代数。此外，我们的模型可转换，可用于具有多种修改的图像域。

恶意软件是跨越多个操作系统和各种文件格式的计算机的最损害威胁之一。为了防止不断增长的恶意软件的威胁，已经提出了巨大的努力来提出各种恶意软件检测方法，试图有效和有效地检测恶意软件。最近的研究表明，一方面，现有的ML和DL能够卓越地检测新出现和以前看不见的恶意软件。然而，另一方面，ML和DL模型本质上易于侵犯对抗性示例形式的对抗性攻击，这通过略微仔细地扰乱了合法输入来混淆目标模型来恶意地产生。基本上，在计算机视觉领域最初广泛地研究了对抗性攻击，并且一些快速扩展到其他域，包括NLP，语音识别甚至恶意软件检测。在本文中，我们专注于Windows操作系统系列中的便携式可执行文件（PE）文件格式的恶意软件，即Windows PE恶意软件，作为在这种对抗设置中研究对抗性攻击方法的代表性案例。具体而言，我们首先首先概述基于ML / DL的Windows PE恶意软件检测的一般学习框架，随后突出了在PE恶意软件的上下文中执行对抗性攻击的三个独特挑战。然后，我们进行全面和系统的审查，以对PE恶意软件检测以及增加PE恶意软件检测的稳健性的相应防御，对近最新的对手攻击进行分类。我们首先向Windows PE恶意软件检测的其他相关攻击结束除了对抗对抗攻击之外，然后对未来的研究方向和机遇脱落。

Deep learning on graph structures has shown exciting results in various applications. However, few attentions have been paid to the robustness of such models, in contrast to numerous research work for image or text adversarial attack and defense. In this paper, we focus on the adversarial attacks that fool the model by modifying the combinatorial structure of data. We first propose a reinforcement learning based attack method that learns the generalizable attack policy, while only requiring prediction labels from the target classifier. Also, variants of genetic algorithms and gradient methods are presented in the scenario where prediction confidence or gradients are available. We use both synthetic and real-world data to show that, a family of Graph Neural Network models are vulnerable to these attacks, in both graph-level and node-level classification tasks. We also show such attacks can be used to diagnose the learned classifiers.

可靠的皮肤癌诊断模型在早期筛查和医疗干预中起着至关重要的作用。流行的计算机辅助皮肤癌分类系统采用深度学习方法。然而，最近的研究揭示了它们对对抗攻击的极端脆弱性 - 通常无法察觉地扰动，可显着降低皮肤癌诊断模型的性能。为了减轻这些威胁，这项工作通过在皮肤癌图像中进行反向对抗性扰动提出了一个简单，有效和资源有效的防御框架。具体而言，首先建立了多尺度图像金字塔，以更好地保留医学成像域中的判别结构。为了中和对抗性效应，通过注射各向同性高斯噪声将不同尺度的皮肤图像逐渐扩散，以将对抗性示例移至干净的图像歧管。至关重要的是，为了进一步逆转对抗性的噪音并抑制了冗余的注射噪声，精心设计了一种新型的多尺度降级机制，可以从相邻尺度汇总图像信息。我们评估了方法对ISIC 2019的防御效果，这是最大的皮肤癌多类分类数据集。实验结果表明，所提出的方法可以成功地逆转不同攻击的对抗扰动，并且在捍卫皮肤癌诊断模型中的某些最新方法明显优于某些最先进的方法。

先前的研究证明，黑盒模型的功能可以被完全概率输出偷走。但是，在更实用的硬牌环境下，我们观察到现有的方法遭受灾难性的性能降解。我们认为这是由于概率预测中缺乏丰富的信息以及硬标签引起的过度拟合。为此，我们提出了一种称为\ emph {black-box disector}的新型硬标签模型窃取方法，该方法由两个基于擦除的模块组成。一种是一种凸轮驱动的擦除策略，旨在增加受害者模型中隐藏在硬标签中的信息能力。另一个是一个基于随机的自我知识蒸馏模块，该模块利用替代模型的软标签来减轻过度拟合。在四个广泛使用的数据集上进行的广泛实验始终表明，我们的方法优于最先进的方法，最多提高了$ 8.27 \％$。我们还验证了我们方法对现实世界API和防御方法的有效性和实际潜力。此外，我们的方法促进了其他下游任务，\ emph {i.e。}，转移对抗攻击。

Deep hashing has been extensively utilized in massive image retrieval because of its efficiency and effectiveness. However, deep hashing models are vulnerable to adversarial examples, making it essential to develop adversarial defense methods for image retrieval. Existing solutions achieved limited defense performance because of using weak adversarial samples for training and lacking discriminative optimization objectives to learn robust features. In this paper, we present a min-max based Center-guided Adversarial Training, namely CgAT, to improve the robustness of deep hashing networks through worst adversarial examples. Specifically, we first formulate the center code as a semantically-discriminative representative of the input image content, which preserves the semantic similarity with positive samples and dissimilarity with negative examples. We prove that a mathematical formula can calculate the center code immediately. After obtaining the center codes in each optimization iteration of the deep hashing network, they are adopted to guide the adversarial training process. On the one hand, CgAT generates the worst adversarial examples as augmented data by maximizing the Hamming distance between the hash codes of the adversarial examples and the center codes. On the other hand, CgAT learns to mitigate the effects of adversarial samples by minimizing the Hamming distance to the center codes. Extensive experiments on the benchmark datasets demonstrate the effectiveness of our adversarial training algorithm in defending against adversarial attacks for deep hashing-based retrieval. Compared with the current state-of-the-art defense method, we significantly improve the defense performance by an average of 18.61%, 12.35%, and 11.56% on FLICKR-25K, NUS-WIDE, and MS-COCO, respectively.

共同突出的对象检测（Cosod）最近实现了重大进展，并在检索相关任务中发挥了关键作用。但是，它不可避免地构成了完全新的安全问题，即，高度个人和敏感的内容可能会通过强大的COSOD方法提取。在本文中，我们从对抗性攻击的角度解决了这个问题，并确定了一种小说任务：对抗的共同显着性攻击。特别地，给定从包含某种常见和突出对象的一组图像中选择的图像，我们的目标是生成可能误导Cosod方法以预测不正确的共突变区域的侵略性版本。注意，与分类的一般白盒对抗攻击相比，这项新任务面临两种额外的挑战：（1）由于本集团中图像的不同外观，成功率低; （2）Cosod方法的低可转换性由于Cosod管道之间的差异相当差异。为了解决这些挑战，我们提出了第一个黑匣子联合对抗的暴露和噪声攻击（JADENA），在那里我们共同和本地调整图像的曝光和添加剂扰动，根据新设计的高特征级对比度敏感损失功能。我们的方法，没有关于最先进的Cosod方法的任何信息，导致各种共同显着性检测数据集的显着性能下降，并使共同突出的物体无法检测到。这在适当地确保目前在互联网上共享的大量个人照片中可以具有很强的实际效益。此外，我们的方法是用于评估Cosod方法的稳健性的指标的潜力。

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。

我们提出了一种新颖且有效的纯化基于纯化的普通防御方法，用于预处理盲目的白色和黑匣子攻击。我们的方法仅在一般图像上进行了自我监督学习，在计算上效率和培训，而不需要对分类模型的任何对抗训练或再培训。我们首先显示对原始图像与其对抗示例之间的残余的对抗噪声的实证分析，几乎均为对称分布。基于该观察，我们提出了一种非常简单的迭代高斯平滑（GS），其可以有效地平滑对抗性噪声并实现大大高的鲁棒精度。为了进一步改进它，我们提出了神经上下文迭代平滑（NCIS），其以自我监督的方式列举盲点网络（BSN）以重建GS也平滑的原始图像的辨别特征。从我们使用四种分类模型对大型想象成的广泛实验，我们表明我们的方法既竞争竞争标准精度和最先进的强大精度，则针对最强大的净化器 - 盲目的白色和黑匣子攻击。此外，我们提出了一种用于评估基于商业图像分类API的纯化方法的新基准，例如AWS，Azure，Clarifai和Google。我们通过基于集合转移的黑匣子攻击产生对抗性实例，这可以促进API的完全错误分类，并证明我们的方法可用于增加API的抗逆性鲁棒性。

Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting. This is intrinsically an NP-hard knapsack problem where the exploration space becomes explosively larger as the feature dimension increases. Without the help of domain knowledge, solving this problem via heuristic method, such as Branch-and-Bound, suffers from exponential complexity, yet can bring arbitrarily bad attack results. We address the challenge via the lens of multi-armed bandit based combinatorial search. Our proposed method, namely FEAT, treats modifying each categorical feature as pulling an arm in multi-armed bandit programming. Our objective is to achieve highly efficient and effective attack using an Orthogonal Matching Pursuit (OMP)-enhanced Upper Confidence Bound (UCB) exploration strategy. Our theoretical analysis bounding the regret gap of FEAT guarantees its practical attack performance. In empirical analysis, we compare FEAT with other state-of-the-art domain-agnostic attack methods over various real-world categorical data sets of different applications. Substantial experimental observations confirm the expected efficiency and attack effectiveness of FEAT applied in different application scenarios. Our work further hints the applicability of FEAT for assessing the adversarial vulnerability of classification systems with high-dimensional categorical inputs.