What is the translation of " ADVERSARIAL MACHINE LEARNING " in Chinese?

• 对抗性机器学习
• 敌对机器学习

Examples of using Adversarial machine learning in English and their translations into Chinese

Adversarial Machine Learning.
对抗机器学习.

McAfee, Grobman said, is studying“adversarial machine learning.”.
Grobman说，McAfee正在学习「对抗性机器学习技术」。

Adversarial Machine Learning.
抗性机器学习.

What are some new and exciting areas in adversarial machine learning research?
对抗机器学习研究有什么新的和令人兴奋的领域？?

Adversarial machine learning for LoT.
浅析机器学习之于IOT.

It's a field called adversarial machine learning, or adversarial AI.
这是一个被称为敌对机器学习的领域，或者是对抗性AI。

Adversarial machine learning is particularly important for security-critical systems.
对抗型的机器学习对安全应用来说相当关键。

It's a field we call adversarial machine learning, or adversarial AI.
这是一个被称为敌对机器学习的领域，或者是对抗性AI。

I hope this is just the beginning of your journey into adversarial machine learning.
最后，我希望这篇文章是你对抗机器学习之旅的开始。

Adversarial machine learning is an area of growing interest for machine-learning researchers.
对抗性机器学习是机器学习研究人员越来越感兴趣的一个领域。

This field of research is called Adversarial Machine Learning.
该研究领域被称为“对抗机器学习”（AdversarialMachineLearning）。

Imagine using this kind of adversarial machine learning on malware classification and other cyber-defense models,” she said.
想象一下，使用这种对抗机器学习恶意软件分类和其他网络防御模型，”她说。

Mittal is a pioneer in understanding an emerging vulnerability known as adversarial machine learning.
Mittal是了解一种新出现的弱点的先驱，这种弱点被称为对抗机器学习。

There's going to be a greater need for adversarial machine learning to combat supply chain corruption in 2020.
对抗机器学习以应对供应链腐败的需求将在2020年增加。

In their recent series of papers, Mittal's group described and demonstrated three broad types of adversarial machine learning attacks.
Mittal的团队在他们最近的一系列论文中，描述并演示了三种主要类型的对抗机器学习攻击。

There's going to be a greater need for adversarial machine learning to combat supply chain corruption in 2020.
其中的一个预测是：2020年，对抗机器学习以应对供应链腐败的需求将越来越大。

In their recent series of papers, Mittal's group described and demonstrated three broad types of adversarial machine learning attacks.
米塔尔(Mittal)的小组在最近的一系列论文中描述并演示了三种广泛的对抗性机器学习攻击。

In which combining deep learning, with adversarial machine learning techniques[17][18] may lay the answer we are looking for.
将深度学习与对抗性机器学习技术相结合[17][18]，可能就是我们要寻找的答案。

Pre-empting criminals attempting to hijack artificial intelligence by tampering with datasets or the physical environment, researchers have turned to adversarial machine learning.
为了防止一些犯罪分子想要通过篡改数据集或现实环境来攻击人工智能，研究人员转向对抗性的机器学习研究。

We can explore ideas from adversarial machine learning and robust statistics to identify and mitigate the effects of maliciously inserted data.
我们可以研究源自对抗性机器学习和稳健统计的思路，找出并减轻恶意插入数据的影响。

Another important focus is addressing research challenges in new application domains such as cybersecurity, transportation-network security, adversarial machine learning, election protection, and nuclear smuggling.
另一个重要的聚焦点是，解决在新应用领域中出现的研究挑战，例如网络安全、运输网络安全、对抗机器学习、选举保护和核走私。

The platform is also designed to detect adversarial machine learning attempts and initiates self-learning to provide protection against unknown threats.
不仅如此，该解决方案还能够开启自我学习，检测对抗性机器学习攻击，提供针对未知威胁的全面保护。

As such, security research into adversarial machine learning and countermeasures aimed at detecting manipulation of ML systems will become critically important.
因此，针对对抗性机器学习的安全性研究以及旨在检测ML系统操纵的对策将变得至关重要。

Attackers will start to leverage adversarial machine learning in their attacks to bypass security products leveraging machine learning for detection of malware," said Alperovitch.
攻击者将开始在攻击中利用对抗性机器学习来绕过安全产品，利用机器学习来检测恶意软件，”Alperovitch说。

Are Machine Learning and Adversarial AI the New Insider Threats?
机器学习和对抗性人工智能会是新的内部威胁吗?

It is early days yet for machine learning and adversarial AI- perhaps early enough that the threats that inevitably materialize will not have the upper hand.
对于与机器学习和对抗人工智能来说，还处于早期阶段，也许来得太早，以至于不可避免地出现的威胁并不会占上风。

A generative adversarial network is a class of machine learning systems.
简而言之，生成式对抗网络是一类机器学习系统。

Adversarial examples are malicious inputs designed to fool machine learning models.
对抗性示例是旨在欺骗机器学习模型的恶意输入。

Her recent research focuses on adversarial deep learning and generative models, as well as designing scalable robust machine learning models against adversarial attacks.
她最近的研究重点是对抗性深度学习和生成模型，以及设计针对对抗性攻击的可扩展的强大机器学习模型。

Machine learning is vulnerable to adversarial attacks.
机器学习模型已经被证明容易受到对抗攻击。