Bringing Engineering Rigor to Deep Learning

Abstract

Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including autonomous driving, robotics, and malware detection, where the correctness and predictability of a system on corner-case inputs are of great importance. Unfortunately, the common practice to validating a deep neural network (DNN) - measuring overall accuracy on a randomly selected test set - is not designed to surface corner-case errors. As recent work shows, even DNNs with state-of-the-art accuracy are easily fooled by human-imperceptible, adversarial perturbations to the inputs. Questions such as how to test corner-case behaviors more thoroughly and whether all adversarial samples have been found remain unanswered. In the last few years, we have been working on bringing more engineering rigor into deep learning. Towards this goal, we have built five systems to test DNNs more thoroughly and verify the absence of adversarial samples for given datasets. These systems check a broad spectrum of properties (e.g., rotating an image should never change its classification) and find thousands of error-inducing samples for popular DNNs in critical domains (e.g., ImageNet, autonomous driving, and malware detection). Our DNN verifiers are also orders of magnitude (e.g., 5,000×) faster than similar tools. This article overviews our systems and discusses three open research challenges to hopefully inspire more future research towards testing and verifying DNNs.