DGFuzz: Region-Level Defect Feedback for Directed Fuzz Testing of Deep Neural Networks

Abstract

Deep neural networks (DNNs) are increasingly used in safety-critical software systems, where incorrect behavior can cause severe consequences. Fuzz testing is an effective technique for automatically exposing such behaviors by generating mutated inputs, but existing DNN fuzzers mainly use neuron-level, critical-neuron, or gradient-level feedback. They rarely exploit the spatial distribution of defects already found during testing. This limits testing efficiency because adversarial defects often concentrate in vulnerable regions of the feature space rather than appearing randomly. This paper presents DGFuzz, a defect-guided directed fuzz testing framework for DNNs. DGFuzz maps discovered adversarial defects to the penultimate-layer feature space and identifies defect-concentrated clusters as vulnerable regions. These vulnerable regions are then reused as testing feedback through two strategies: defect-cluster-guided seed initialization, which selects seeds closer to high-risk regions, and adaptive population scheduling, which allocates mutation resources according to regional defect-discovery potential. We evaluate DGFuzz on 9 DNN models over MNIST, CIFAR-10, and GTSRB and compare it with representative neuron-coverage-, critical-neuron-, and gradient-guided fuzzing methods. DGFuzz discovers more unique defects in 8 of 9 models under a fixed mutation budget and achieves higher fuzzing-phase efficiency than the baselines. Extended-budget and ablation studies further quantify the contributions of vulnerable-region modeling, seed initialization, and adaptive scheduling. Overall, DGFuzz turns discovered defect distributions into reusable feedback for automated DNN testing and improves unique-defect discovery under limited testing budgets.