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AI也能生成指纹,生物识别系统该如何应对?

其他国际资讯

AI也能生成指纹,生物识别系统该如何应对?

众所周知,人类并不十分擅长创建安全密码。

为此,技术人员发明了一系列依赖生物识别身份验证(比如指纹识别、语音识别和面部扫描)的新设备来解决(或者说某种程度上)这个问题。

然而,近日纽约大学和密歇根州立大学的研究人员对生物识别技术是否足够安全提出了质疑。

纽约大学博士生、本次研究主要研究员Phillip Bontrager表示,

“基于指纹的身份验证仍然是保护设备或系统的一种有效方法,但大多数系统都不会验证指纹或其他生物识别特征是来自真人还是复制品。”

而问题就出在大多数指纹传感器的工作方式上。

纽约大学教授纳Nasir Memon以前的研究详细描述了一些系统中的致命缺陷。比如大多数此类系统依靠部分指纹来确认身份,而不是使用完整的指纹。为此,大多数设备允许用户提交多个指纹图像,只要与任何已保存部分的匹配通常就足以确认身份。

最近,研究人员利用这些发现创建了一种新的机器学习算法,通过收集存储在指纹可访问系统中的指纹图像来生成所谓的“指纹”。将来,可能有人会利用这些假冒合成产品来对真实系统或验证设备发起攻击,测试系统库中的所有指纹,直到最终打开设备。

目前,该系统尚未在真实设备上进行测试。

我们相信生物识别设备能够保护我们不断增加的敏感数据,所以我们认为这项发现也同样非常重要。

Research led by two top universities has shed doubt on whether biometric security systems, on their own, can protect our most sensitive data.

Humans are notoriously bad at creating secure passwords. But that’s okay; we’ve fixed the problem, at least somewhat, by introducing a slew of new devices that rely on biometric authentication, whether in the form of fingerprints, voice recognition, or facial scanning.

Researchers at New York University and Michigan State University, however, have their doubts about whether biometrics alone are enough. “Fingerprint-based authentication is still a strong way to protect a device or system, but at this point, most systems don’t verify whether a fingerprint or other biometric is coming from a real person or replica,” said Phillip Bontrager, lead author of the paper and doctoral student at NYU.

At issue is the way in which most fingerprint sensors work. Previous research by NYU professor Nasir Memon detailed a fatal flaw in some system. Rather than using a full fingerprint, most relied on partial fingerprints to confirm identity. Most devices allow users to submit a number of fingerprint images, and a match for any saved partial is often enough to confirm identity.

This led Memon, and Professor Arun Ross, of Michigan State University, to coin the term “MasterPrint” to describe the way partial prints are often enough.

Recently, researchers built upon these findings to create a new machine-learning algorithm that generates synthetic fingerprints. These AI-generated fakes could be pitted against real devices in the near future by harvesting fingerprint images stored in fingerprint-accessible systems. And if that happens, researchers suggest, these fingerprints could use used to launch a brute force attack, testing each fingerprint in a system until it opens a device, or a door, as it may be.

Currently, the system hasn’t been tested on real devices. At this point, it’s purely hypothetical. But the research is undoubtedly important in a day where we’re trusting biometric devices to secure ever-increasing amounts of sensitive data.

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