We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (recognizing up to 50 characters per second of audio). We apply our white-box iterative optimization-based attack to Mozilla's implementation DeepSpeech end-to-end, and show it has a 100% success rate. The feasibility of this attack introduce a new domain to study adversarial examples.

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.

Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy. We show that all can be defeated by constructing new loss functions. We conclude that adversarial examples are significantly harder to detect than previously appreciated, and the properties believed to be intrinsic to adversarial examples are in fact not. Finally, we propose several simple guidelines for evaluating future proposed defenses.

We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimizationbased attacks, we find defenses relying on this effect can be circumvented. We describe characteristic behaviors of defenses exhibiting the effect, and for each of the three types of obfuscated gradients we discover, we develop attack techniques to overcome it. In a case study, examining noncertified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 9 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely, and 1 partially, in the original threat model each paper considers.

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.

随着硬件和算法的开发，ASR（自动语音识别）系统发展了很多。随着模型变得越来越简单，开发和部署的困难变得更加容易，ASR系统正越来越接近我们的生活。一方面，我们经常使用ASR的应用程序或API来生成字幕和记录会议。另一方面，智能扬声器和自动驾驶汽车依靠ASR系统来控制Aiot设备。在过去的几年中，对ASR系统的攻击攻击有很多作品。通过在波形中添加小的扰动，识别结果有很大的不同。在本文中，我们描述了ASR系统的发展，攻击的不同假设以及如何评估这些攻击。接下来，我们在两个攻击假设中介绍了有关对抗性示例攻击的当前作品：白框攻击和黑框攻击。与其他调查不同，我们更多地关注它们在ASR系统中扰动波形，这些攻击之间的关系及其实现方法之间的层。我们专注于他们作品的效果。

深度学习技术的发展极大地促进了自动语音识别（ASR）技术的性能提高，该技术证明了在许多任务中与人类听力相当的能力。语音接口正变得越来越广泛地用作许多应用程序和智能设备的输入。但是，现有的研究表明，DNN很容易受到轻微干扰的干扰，并且会出现错误的识别，这对于由声音控制的智能语音应用非常危险。

对抗商业黑匣子语音平台的对抗攻击，包括云语音API和语音控制设备，直到近年来接受了很少的关注。目前的“黑匣子”攻击所有严重依赖于预测/置信度评分的知识，以加工有效的对抗示例，这可以通过服务提供商直观地捍卫，而不返回这些消息。在本文中，我们提出了在更实用和严格的情况下提出了两种新的对抗攻击。对于商业云演讲API，我们提出了一个决定的黑匣子逆势攻击，这些攻击是唯一的最终决定。在偶变中，我们将决策的AE发电作为一个不连续的大规模全局优化问题，并通过自适应地将该复杂问题自适应地分解成一组子问题并协同优化每个问题来解决它。我们的春天是一种齐全的所有方法，它在一个广泛的流行语音和扬声器识别API，包括谷歌，阿里巴巴，微软，腾讯，达到100％的攻击攻击速度100％的攻击率。 iflytek，和景东，表现出最先进的黑箱攻击。对于商业语音控制设备，我们提出了Ni-Occam，第一个非交互式物理对手攻击，而对手不需要查询Oracle并且无法访问其内部信息和培训数据。我们将对抗性攻击与模型反演攻击相结合，从而产生具有高可转换性的物理有效的音频AE，而无需与目标设备的任何交互。我们的实验结果表明，NI-Occam可以成功欺骗苹果Siri，Microsoft Cortana，Google Assistant，Iflytek和Amazon Echo，平均SRO为52％和SNR为9.65dB，对抗语音控制设备的非交互式物理攻击。

发言人识别系统（SRSS）最近被证明容易受到对抗攻击的影响，从而引发了重大的安全问题。在这项工作中，我们系统地研究了基于确保SRSS的基于对抗性训练的防御。根据SRSS的特征，我们提出了22种不同的转换，并使用扬声器识别的7种最新有前途的对抗攻击（4个白盒和3个Black-Box）对其进行了彻底评估。仔细考虑了国防评估中的最佳实践，我们分析了转换的强度以承受适应性攻击。我们还评估并理解它们与对抗训练相结合的自适应攻击的有效性。我们的研究提供了许多有用的见解和发现，其中许多与图像和语音识别域中的结论是新的或不一致的，例如，可变和恒定的比特率语音压缩具有不同的性能，并且某些不可差的转换仍然有效地抗衡。当前有希望的逃避技术通常在图像域中很好地工作。我们证明，与完整的白色盒子设置中的唯一对抗性训练相比，提出的新型功能级转换与对抗训练相比是相当有效的，例如，将准确性提高了13.62％，而攻击成本则达到了两个数量级，而其他攻击成本则增加了。转型不一定会提高整体防御能力。这项工作进一步阐明了该领域的研究方向。我们还发布了我们的评估平台SpeakerGuard，以促进进一步的研究。

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.

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.

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.

最近的工作阐明了说话者识别系统（SRSS）针对对抗性攻击的脆弱性，从而在部署SRSS时引起了严重的安全问题。但是，他们仅考虑了一些设置（例如，来源和目标扬声器的某些组合），仅在现实世界攻击方案中留下了许多有趣而重要的环境。在这项工作中，我们介绍了AS2T，这是该域中的第一次攻击，该域涵盖了所有设置，因此，对手可以使用任意源和目标扬声器来制作对抗性声音，并执行三个主要识别任务中的任何一种。由于现有的损失功能都不能应用于所有设置，因此我们探索了每种设置的许多候选损失功能，包括现有和新设计的损失功能。我们彻底评估了它们的功效，并发现某些现有的损失功能是次优的。然后，为了提高AS2T对实用的无线攻击的鲁棒性，我们研究了可能发生的扭曲发生在空中传输中，利用具有不同参数的不同转换功能来对这些扭曲进行建模，并将其整合到生成中对手的声音。我们的模拟无线评估验证了解决方案在产生强大的对抗声音方面的有效性，这些声音在各种硬件设备和各种声音环境下保持有效，具有不同的混响，环境噪声和噪声水平。最后，我们利用AS2T来执行迄今为止最大的评估，以了解14个不同SRSS之间的可转移性。可传递性分析提供了许多有趣且有用的见解，这些见解挑战了图像域中先前作品中得出的几个发现和结论。我们的研究还阐明了说话者识别域中对抗攻击的未来方向。

自动语音识别系统为应用程序创建了激动人心的可能性，但是它们还为系统窃听的机会提供了机会。我们提出了一种方法来伪装一个人的声音，这些系统来自这些系统，而不会对房间里的人之间的谈话不方便。标准对策攻击在实时流动情况下无效，因为信号的特性将在执行攻击时发生变化。我们介绍了预测攻击，通过预测将来最有效的攻击预测攻击来实现实时性能。在实时约束下，我们的方法在通过字错误率通过字错误率测量的基本电咨询器中，我们的方法堵塞了37x的基线，而通过字符错误率测量。我们还展示了我们的方法在物理环境中实际上是在物理距离的现实环境中。

This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models-a common type of machine-learning model. Because such models are sometimes trained on sensitive data (e.g., the text of users' private messages), this methodology can benefit privacy by allowing deep-learning practitioners to select means of training that minimize such memorization.In experiments, we show that unintended memorization is a persistent, hard-to-avoid issue that can have serious consequences. Specifically, for models trained without consideration of memorization, we describe new, efficient procedures that can extract unique, secret sequences, such as credit card numbers. We show that our testing strategy is a practical and easy-to-use first line of defense, e.g., by describing its application to quantitatively limit data exposure in Google's Smart Compose, a commercial text-completion neural network trained on millions of users' email messages.

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.

Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations. We synthesize two-dimensional adversarial images that are robust to noise, distortion, and affine transformation. We apply our algorithm to complex three-dimensional objects, using 3D-printing to manufacture the first physical adversarial objects. Our results demonstrate the existence of 3D adversarial objects in the physical world.

Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full output of the network on arbitrarily many chosen inputs. We define three realistic threat models that more accurately characterize many real-world classifiers: the query-limited setting, the partialinformation setting, and the label-only setting. We develop new attacks that fool classifiers under these more restrictive threat models, where previous methods would be impractical or ineffective. We demonstrate that our methods are effective against an ImageNet classifier under our proposed threat models. We also demonstrate a targeted black-box attack against a commercial classifier, overcoming the challenges of limited query access, partial information, and other practical issues to break the Google Cloud Vision API.

几年的研究表明，在理论和实践中，机器学习系统容易受到对抗的例子。到目前为止，这种攻击主要有针对性的视觉模型，利用人类和机器感知之间的差距。虽然基于文本的模型也被对抗例子遭到攻击，但这种攻击努力保持语义意义和无法区分。在本文中，我们探讨了大类的对抗示例，可用于在黑盒设置中攻击基于文本的模型，而不会对输入进行任何人类可知的视觉修改。我们使用对人眼不可察觉的编码特异性扰动来操纵从神经计算机翻译管道到网络搜索引擎的各种自然语言处理（NLP）系统的输出。我们发现，通过单一的难以察觉的编码注射 - 代表一个无形的字符，同型角色，重新排序或删除 - 攻击者可以显着降低易受伤害的模型的性能，并且三次注射大多数型号可以在功能上打破。除了由Facebook，IBM和HuggingFace发布的开源模型之外，我们攻击目前部署的商业系统这一新颖的一系列攻击对许多语言处理系统提供了重大威胁：攻击者可以以目标方式影响系统而没有任何关于底层模型的假设。我们得出结论，基于文本的NLP系统需要仔细的输入消毒，就像传统应用程序一样，并且考虑到这样的系统现在正在迅速地部署，需要建筑师和运营商的紧急注意。