Deep neural networks have emerged as a widely used and effective means for
tackling complex, real-world problems. However, a major obstacle in applying
them to safety-critical systems is the great difficulty in providing formal
guarantees about their behavior. We present a novel, scalable, and efficient
technique for verifying properties of deep neural networks (or providing
counter-examples). The technique is based on the simplex method, extended to
handle the non-convex Rectified Linear Unit (ReLU) activation function, which
is a crucial ingredient in many modern neural networks. The verification
procedure tackles neural networks as a whole, without making any simplifying
assumptions. We evaluated our technique on a prototype deep neural network
implementation of the next-generation airborne collision avoidance system for
unmanned aircraft (ACAS Xu). Results show that our technique can successfully
prove properties of networks that are an order of magnitude larger than the
largest networks verified using existing methods.

我们提出了一种基于单纯形方法的技术，用于验证深度神经网络的属性，该方法可以处理非凸的修正线性单元（ReLU）激活函数，成功地验证相对于现有方法已知的规模更大的神经网络，以及用于无人机的下一代空中碰撞避免系统（ACAS Xu）的深度神经网络原型实现。