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StaRFuzz: Stability-Aware Fuzzing for Deep Neural Networks via Multi-Granularity Internal Responses

https://doi.org/10.2139/ssrn.7299992
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Outgoing claims: the papers this work cites, builds on, or argues with.

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A review of convolutional neural networks in computer vision
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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.dsp.2017.10.011openalex-snapshot
Methods for interpreting and understanding deep neural networks
inferredcites →https://doi.org/10.1016/j.jss.2010.11.920openalex-snapshot
Testing and validating machine learning classifiers by metamorphic testing
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/qrs-c55045.2021.00021openalex-snapshot
The Effect of Combinatorial Coverage for Neurons on Fault Detection in Deep Neural Networks
inferredcites →https://doi.org/10.1109/saner50967.2021.00026openalex-snapshot
DeepCon: Contribution Coverage Testing for Deep Learning Systems
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/tnsm.2021.3120804openalex-snapshot
Network Abnormal Traffic Detection Model Based on Semi-Supervised Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/tse.2019.2946563openalex-snapshot
The Art, Science, and Engineering of Fuzzing: A Survey
inferredcites →https://doi.org/10.1109/tse.2019.2962027openalex-snapshot
Machine Learning Testing: Survey, Landscapes and Horizons
inferredcites →https://doi.org/10.1109/tse.2023.3349001openalex-snapshot
An Empirical Study on Correlations Between Deep Neural Network Fairness and Neuron Coverage Criteria
inferredcites →https://doi.org/10.1145/267580.267590openalex-snapshot
Software unit test coverage and adequacy
inferredcites →https://doi.org/10.1145/3238147.3238202openalex-snapshot
DeepGauge: multi-granularity testing criteria for deep learning systems
inferredcites →https://doi.org/10.1145/3293882.3330579openalex-snapshot
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
inferredcites →https://doi.org/10.1145/3368089.3409671openalex-snapshot
Correlations between deep neural network model coverage criteria and model quality
inferredcites →https://doi.org/10.1145/3368089.3409754openalex-snapshot
Is neuron coverage a meaningful measure for testing deep neural networks?
inferredcites →https://doi.org/10.1145/3377811.3380422openalex-snapshot
DeepBillboard
inferredcites →https://doi.org/10.1145/3490489openalex-snapshot
NPC: N euron P ath C overage via Characterizing Decision Logic of Deep Neural Networks
inferredcites →https://doi.org/10.1145/3569935openalex-snapshot
Simulator-based Explanation and Debugging of Hazard-triggering Events in DNN-based Safety-critical Systems
inferredcites →https://doi.org/10.1145/3625290openalex-snapshot
Hierarchical Distribution-aware Testing of Deep Learning
inferredcites →https://doi.org/10.1145/3643678openalex-snapshot
Test Optimization in DNN Testing: A Survey
inferredcites →https://doi.org/10.1145/3650212.3680385openalex-snapshot
See the Forest, not Trees: Unveiling and Escaping the Pitfalls of Error-Triggering Inputs in Neural Network Testing
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