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FuzzInsight improves DNN fuzzing with new sampling and coverage strategies

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FuzzInsight, a new framework designed to improve the efficiency and effectiveness of fuzzing techniques for Deep Neural Networks (DNNs). The system addresses bottlenecks in seed utilization and coverage guidance by introducing an Equalized Sampling strategy, which enhances mean seed queue utilization by 13 percentage points. FuzzInsight also identifies non-structural coverage criteria like LSCD that offer comparable results to structural methods with less computational overhead, promoting broader behavioral exploration of DNNs. The framework's generated corner cases can improve generalization robustness by up to 77 percentage points when used for retraining. AI

IMPACT Enhances DNN robustness and generalization by improving testing efficiency and corner-case generation.

RANK_REASON The item is a research paper detailing a new method for DNN fuzzing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FuzzInsight improves DNN fuzzing with new sampling and coverage strategies

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The item is a research paper detailing a new method for DNN fuzzing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Vivek Vekariya, Fathiyeh Faghih, Alexander Pretschner ·

    FuzzInsight: Navigating the Efficiency-Effectiveness Trade-off in Coverage-Guided DNN Fuzzing

    arXiv:2610.23195v1 Announce Type: new Abstract: Collecting and labeling diverse test datasets for evaluating Deep Neural Networks (DNNs) is costly and time-consuming. This has motivated coverage-guided fuzzing techniques that generate defect-revealing corner cases from limited te…

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