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Ex 2 : Monte Carlo Tree Search‐based test inputs prioritization for fuzzing deep neural networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s10514-017-9626-0openalex-snapshot
Monte Carlo planning for active object classification
inferredcites →https://doi.org/10.1016/j.chroma.2017.08.007openalex-snapshot
Preparation and evaluation of an adsorbent based on poly (muconic acid-co-divinylbenzene/ethylenedimethacrylate) for multiple monolithic fiber solid-phase microextraction of tetracycline antibiotics
inferredcites →https://doi.org/10.1016/j.cosrev.2020.100270openalex-snapshot
A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
inferredcites →https://doi.org/10.1016/j.tcs.2019.05.046openalex-snapshot
A game-based approximate verification of deep neural networks with provable guarantees
inferredcites →https://doi.org/10.1038/nature16961openalex-snapshot
Mastering the game of Go with deep neural networks and tree search
inferredcites →https://doi.org/10.1109/32.962562openalex-snapshot
Prioritizing test cases for regression testing
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/aitest.2019.00013openalex-snapshot
DeepPath: Path-Driven Testing Criteria for Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2012.6248110openalex-snapshot
Multi-column deep neural networks for image classification
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/icse.2019.00108openalex-snapshot
Guiding Deep Learning System Testing Using Surprise Adequacy
inferredcites →https://doi.org/10.1109/icse43902.2021.00046openalex-snapshot
Prioritizing Test Inputs for Deep Neural Networks via Mutation Analysis
inferredcites →https://doi.org/10.1109/issrew51248.2020.00055openalex-snapshot
Using Metamorphic Testing to Evaluate DNN Coverage Criteria
inferredcites →https://doi.org/10.1109/qrs51102.2020.00059openalex-snapshot
PEACEPACT: Prioritizing Examples to Accelerate Perturbation-Based Adversary Generation for DNN Classification Testing
inferredcites →https://doi.org/10.1109/saner.2019.8668044openalex-snapshot
DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems
inferredcites →https://doi.org/10.1109/sp.2018.00040openalex-snapshot
CollAFL: Path Sensitive Fuzzing
inferredcites →https://doi.org/10.1109/tciaig.2012.2186810openalex-snapshot
A Survey of Monte Carlo Tree Search Methods
inferredcites →https://doi.org/10.1145/1146238.1146242openalex-snapshot
Coverage metrics for requirements-based testing
inferredcites →https://doi.org/10.1145/3213846.3213858openalex-snapshot
Identifying implementation bugs in machine learning based image classifiers using metamorphic testing
inferredcites →https://doi.org/10.1145/3238147.3238202openalex-snapshot
DeepGauge: multi-granularity testing criteria for deep learning systems
inferredcites →https://doi.org/10.1145/3241979openalex-snapshot
Metamorphic testing of driverless cars
inferredcites →https://doi.org/10.1145/3293882.3330579openalex-snapshot
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
inferredcites →https://doi.org/10.1145/3395363.3397357openalex-snapshot
DeepGini: prioritizing massive tests to enhance the robustness of deep neural networks
inferredcites →https://doi.org/10.1145/3417330openalex-snapshot
Test Selection for Deep Learning Systems
inferredcites →https://doi.org/10.1145/96267.96279openalex-snapshot
An empirical study of the reliability of UNIX utilities
inferredcites →https://doi.org/10.14722/ndss.2017.23404openalex-snapshot
VUzzer: Application-aware Evolutionary Fuzzing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4647085openalex-extracted
Criticalfuzz: A Critical Neuron Coverage-Guided Fuzz Testing Framework for Deep Neural Networks
inferred← citeshttps://doi.org/10.1016/j.infsof.2024.107476openalex-snapshot
CriticalFuzz: A critical neuron coverage-guided fuzz testing framework for deep neural networks
inferred← citeshttps://doi.org/10.1016/j.procs.2026.06.501openalex-snapshot
Uncertainty–Interpretability Fusion for Prioritization of Metamorphic Tests in Deep Learning
inferred← citeshttps://doi.org/10.1038/s41598-026-49010-3openalex-snapshot
Class-adaptive oracle-free metamorphic test case prioritization framework for vision-based deep neural networks
inferred← citeshttps://doi.org/10.1109/trustcom66490.2025.00246openalex-snapshot
GMFuzz: Integrating Hierarchical Mutation and Fidelity Constraints for Coverage-Guided DNN Security Testing
inferred← citeshttps://doi.org/10.14569/ijacsa.2024.0150576openalex-snapshot
Enhancing Smart Contract Security Through Multi-Agent Deep Reinforcement Learning Fuzzing: A Survey of Approaches and Techniques
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