Symbolic Execution for Deep Neural Networks

TL;DR

DeepCheck leverages symbolic execution to validate deep neural networks by translating them into programs, enabling analysis for important pixels and adversarial attacks, demonstrated effectively on MNIST data.

Contribution

The paper introduces DeepCheck, a novel approach that applies symbolic execution to DNNs by translating them into programs for validation and attack generation.

Findings

Lightweight symbolic analysis effectively identifies important pixels.

DeepCheck successfully generates 1-pixel and 2-pixel attacks.

Experimental results on MNIST validate the approach's usefulness.

Abstract

Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with substantial safety and security concerns. This paper introduces DeepCheck, a new approach for validating DNNs based on core ideas from program analysis, specifically from symbolic execution. The idea is to translate a DNN into an imperative program, thereby enabling program analysis to assist with DNN validation. A basic translation however creates programs that are very complex to analyze. DeepCheck introduces novel techniques for lightweight symbolic analysis of DNNs and applies them in the context of image classification to address two challenging problems in DNN analysis: 1) identification of important pixels (for attribution and adversarial generation); and 2) creation of 1-pixel and 2-pixel attacks. Experimental results using the MNIST data-set show that DeepCheck's lightweight symbolic analysis provides a valuable tool for DNN validation.