论文标题
保护神经网络训练程序的完整性
Protecting the integrity of the training procedure of neural networks
论文作者
论文摘要
由于近年来性能的显着改善，目前使用神经网络用于数量不断增加的应用程序。但是，神经网络的弊端是，他们的决策不容易解释，并且可以追溯到人类。这会造成多个问题，例如，在高风险应用程序的安全性和IT安全方面，确保这些属性至关重要。神经网络不透明性加剧的最引人注目的IT安全问题之一是在训练阶段可能会发生所谓的中毒攻击，在训练阶段，攻击者插入了专门制作的数据以操纵所得模型。我们提出了一种解决此问题的方法，该方法可以通过使用标准加密机制来证明验证训练程序的完整性。
Due to significant improvements in performance in recent years, neural networks are currently used for an ever-increasing number of applications. However, neural networks have the drawback that their decisions are not readily interpretable and traceable for a human. This creates several problems, for instance in terms of safety and IT security for high-risk applications, where assuring these properties is crucial. One of the most striking IT security problems aggravated by the opacity of neural networks is the possibility of so-called poisoning attacks during the training phase, where an attacker inserts specially crafted data to manipulate the resulting model. We propose an approach to this problem which allows provably verifying the integrity of the training procedure by making use of standard cryptographic mechanisms.
