Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

TL;DR

Reluplex is a novel SMT solver that efficiently verifies properties of deep neural networks with ReLU activations, enabling formal guarantees for safety-critical applications like aircraft collision avoidance.

Contribution

It introduces a scalable, ReLU-specific extension of the simplex method for neural network verification, handling larger networks without simplifying assumptions.

Findings

Successfully verified larger networks than previous methods

Proved properties of neural networks used in aircraft safety systems

Demonstrated efficiency and scalability of the approach

Abstract

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.