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DLFuzz: differential fuzzing testing of deep learning systems

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-63387-9_1openalex-snapshot

Safety Verification of Deep Neural Networks

inferredcites →https://doi.org/10.1007/978-3-319-89960-2_22openalex-snapshot

Feature-Guided Black-Box Safety Testing of Deep Neural Networks

inferredcites →https://doi.org/10.1038/nature12346openalex-snapshot

Connectomic reconstruction of the inner plexiform layer in the mouse retina

inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot

Human-level control through deep reinforcement learning

inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot

Gradient-based learning applied to document recognition

inferredcites →https://doi.org/10.1109/9780470544976.ch9openalex-snapshot

GradientBased Learning Applied to Document Recognition

inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot

ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2016.282openalex-snapshot

DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/saner.2018.8330260openalex-snapshot

Fuzz testing in practice: Obstacles and solutions

inferredcites →https://doi.org/10.1145/2619239.2631434openalex-snapshot

Droid-Sec

inferredcites →https://doi.org/10.1145/2740070.2631434openalex-snapshot

Droid-Sec

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1145/3132747.3132785openalex-snapshot

DeepXplore

inferredcites →https://doi.org/10.1145/3180155.3180220openalex-snapshot

DeepTest

inferredcites →https://doi.org/10.1145/3183440.3183494openalex-snapshot

SAFL

inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot

Intriguing properties of neural networks

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1409.3215openalex-snapshot

Sequence to Sequence Learning with Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.1412.5068openalex-snapshot

Towards Deep Neural Network Architectures Robust to Adversarial Examples

inferredcites →https://doi.org/10.48550/arxiv.1604.07316openalex-snapshot

End to End Learning for Self-Driving Cars

inferredcites →https://doi.org/10.48550/arxiv.1708.08559openalex-snapshot

DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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EVMFuzz: Differential fuzz testing of Ethereum virtual machine

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Coverage-guided fuzzing for deep reinforcement learning systems

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DeepKernel: 2D-kernels clustering based mutant reduction for cost-effective deep learning model testing

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RNNtcs: A test case selection method for Recurrent Neural Networks

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GradFuzz: Fuzzing deep neural networks with gradient vector coverage for adversarial examples

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inferred← citeshttps://doi.org/10.1109/qrs62785.2024.00014openalex-snapshot

EATS: Efficient Adaptive Test Case Selection for Deep Neural Networks

inferred← citeshttps://doi.org/10.1109/saner53432.2022.00110openalex-snapshot

Detect, Fix, and Verify TensorFlow API Misuses

inferred← citeshttps://doi.org/10.1109/seaa.2019.00026openalex-snapshot

Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks

inferred← citeshttps://doi.org/10.1109/sist58284.2023.10223540openalex-snapshot

Application of Deep Learning in the Problem of Image Restoration

inferred← citeshttps://doi.org/10.1109/sose52839.2021.00028openalex-snapshot

Model-Based Test Modeling and Automation Tool for Intelligent Mobile Apps

inferred← citeshttps://doi.org/10.1109/tai.2024.3351798openalex-snapshot

A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence

inferred← citeshttps://doi.org/10.1109/tcad.2023.3263120openalex-snapshot

Testing and Enhancing Adversarial Robustness of Hyperdimensional Computing

inferred← citeshttps://doi.org/10.1109/tdsc.2021.3088661openalex-snapshot

Taking Care of the Discretization Problem: A Comprehensive Study of the Discretization Problem and a Black-Box Adversarial Attack in Discrete Integer Domain

inferred← citeshttps://doi.org/10.1109/tdsc.2022.3200421openalex-snapshot

Self-Checking Deep Neural Networks for Anomalies and Adversaries in Deployment

inferred← citeshttps://doi.org/10.1109/tifs.2025.3607249openalex-snapshot

VPGFuzz: Vulnerable Path-Guided Greybox Fuzzing

inferred← citeshttps://doi.org/10.1109/tii.2022.3211888openalex-snapshot

Toward the Trustworthiness of Industrial Robotics Using Differential Fuzz Testing

inferred← citeshttps://doi.org/10.1109/tits.2024.3421343openalex-snapshot

Generation of Risky Scenarios for Testing Automated Driving Visual Perception Based on Causal Analysis

inferred← citeshttps://doi.org/10.1109/tnnls.2021.3130904openalex-snapshot

AMITE: A Novel Polynomial Expansion for Analyzing Neural Network Nonlinearities

inferred← citeshttps://doi.org/10.1109/tnnls.2022.3156620openalex-snapshot

A White-Box Testing for Deep Neural Networks Based on Neuron Coverage

inferred← citeshttps://doi.org/10.1109/tnse.2020.2997359openalex-snapshot

Coverage Guided Differential Adversarial Testing of Deep Learning Systems

inferred← citeshttps://doi.org/10.1109/tr.2021.3080664openalex-snapshot

Coverage-Guided Testing for Recurrent Neural Networks

inferred← citeshttps://doi.org/10.1109/tr.2021.3107165openalex-snapshot

Duo: Differential Fuzzing for Deep Learning Operators

inferred← citeshttps://doi.org/10.1109/tr.2022.3208239openalex-snapshot

CoCoFuzzing: Testing Neural Code Models With Coverage-Guided Fuzzing

inferred← citeshttps://doi.org/10.1109/tr.2023.3261986openalex-snapshot

Cube-Evo: A Query-Efficient Black-Box Attack on Video Classification System

inferred← citeshttps://doi.org/10.1109/tr.2023.3322406openalex-snapshot

DFuzzer: Diversity-Driven Seed Queue Construction of Fuzzing for Deep Learning Models

inferred← citeshttps://doi.org/10.1109/tr.2025.3607784openalex-snapshot

Adaptive Random Testing of Deep Learning Systems Using Image Hashing

inferred← citeshttps://doi.org/10.1109/tse.2019.2946563openalex-snapshot

The Art, Science, and Engineering of Fuzzing: A Survey

inferred← citeshttps://doi.org/10.1109/tse.2019.2962027openalex-snapshot

Machine Learning Testing: Survey, Landscapes and Horizons

inferred← citeshttps://doi.org/10.1109/tse.2020.3034721openalex-snapshot

Towards Security Threats of Deep Learning Systems: A Survey

inferred← citeshttps://doi.org/10.1109/tse.2021.3114353openalex-snapshot

RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems

inferred← citeshttps://doi.org/10.1109/tse.2021.3124006openalex-snapshot

CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing for Image-Based Deep Learning Systems

inferred← citeshttps://doi.org/10.1109/tse.2022.3206427openalex-snapshot

A Declarative Metamorphic Testing Framework for Autonomous Driving

inferred← citeshttps://doi.org/10.1109/tse.2023.3301443openalex-snapshot

DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles

inferred← citeshttps://doi.org/10.1109/tse.2023.3323969openalex-snapshot

Differential Testing of Machine Translators Based on Compositional Semantics

inferred← citeshttps://doi.org/10.1109/tse.2023.3330982openalex-snapshot

Robust Test Selection for Deep Neural Networks

inferred← citeshttps://doi.org/10.1109/tse.2024.3463747openalex-snapshot

Metamorphic Testing of Image Captioning Systems via Image-Level Reduction

inferred← citeshttps://doi.org/10.1109/tse.2025.3622251openalex-snapshot

DT4LM: Differential Testing for Reliable Language Model Updates in Classification Tasks

inferred← citeshttps://doi.org/10.1109/tse.2026.3675285openalex-snapshot

How Composite Metamorphic Relations Enhance Test Effectiveness of DNN Testing: An Empirical Study

inferred← citeshttps://doi.org/10.1109/wacv51458.2022.00292openalex-snapshot

Fair and accurate age prediction using distribution aware data curation and augmentation

inferred← citeshttps://doi.org/10.1109/wintechcon66724.2025.11430034openalex-snapshot

The Sub Band-based Randomizer (SBR): A Novel Approach to Balanced Randomization for Vast Configuration Spaces

inferred← citeshttps://doi.org/10.1145/3295500.3356177openalex-snapshot

BinFI

inferred← citeshttps://doi.org/10.1145/3338906.3341175openalex-snapshot

EVMFuzzer: detect EVM vulnerabilities via fuzz testing

inferred← citeshttps://doi.org/10.1145/3358227openalex-snapshot

Polar

inferred← citeshttps://doi.org/10.1145/3368089.3409671openalex-snapshot

Correlations between deep neural network model coverage criteria and model quality

inferred← citeshttps://doi.org/10.1145/3368089.3409730openalex-snapshot

Model-based exploration of the frontier of behaviours for deep learning system testing

inferred← citeshttps://doi.org/10.1145/3368089.3409750openalex-snapshot

DeepSearch: a simple and effective blackbox attack for deep neural networks

inferred← citeshttps://doi.org/10.1145/3371158.3371233openalex-snapshot

Tutorial on Software Testing & Quality Assurance for Machine Learning Applications from research bench to real world

inferred← citeshttps://doi.org/10.1145/3377811.3380363openalex-snapshot

HyDiff

inferred← citeshttps://doi.org/10.1145/3377811.3380379openalex-snapshot

Dissector

inferred← citeshttps://doi.org/10.1145/3394112openalex-snapshot

Practical Accuracy Estimation for Efficient Deep Neural Network Testing

inferred← citeshttps://doi.org/10.1145/3395363.3397346openalex-snapshot

Effective white-box testing of deep neural networks with adaptive neuron-selection strategy

inferred← citeshttps://doi.org/10.1145/3457913.3457942openalex-snapshot

Testing for Dynamic Software Update: An Object-State-Oriented Approach

inferred← citeshttps://doi.org/10.1145/3460319.3464801openalex-snapshot

Exposing previously undetectable faults in deep neural networks

inferred← citeshttps://doi.org/10.1145/3460319.3464802openalex-snapshot

Automatic test suite generation for key-points detection DNNs using many-objective search (experience paper)

inferred← citeshttps://doi.org/10.1145/3460319.3464809openalex-snapshot

AdvDoor: adversarial backdoor attack of deep learning system

inferred← citeshttps://doi.org/10.1145/3460319.3464811openalex-snapshot

DeepHyperion: exploring the feature space of deep learning-based systems through illumination search

inferred← citeshttps://doi.org/10.1145/3468264.3468612openalex-snapshot

Exposing numerical bugs in deep learning via gradient back-propagation

inferred← citeshttps://doi.org/10.1145/3468264.3473131openalex-snapshot

Selecting test inputs for DNNs using differential testing with subspecialized model instances

inferred← citeshttps://doi.org/10.1145/3485275openalex-snapshot

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research

inferred← citeshttps://doi.org/10.1145/3510003.3510044openalex-snapshot

R2Z2

inferred← citeshttps://doi.org/10.1145/3510003.3510092openalex-snapshot

Muffin

inferred← citeshttps://doi.org/10.1145/3510003.3510191openalex-snapshot

ReMoS

inferred← citeshttps://doi.org/10.1145/3510003.3510232openalex-snapshot

Adaptive test selection for deep neural networks

inferred← citeshttps://doi.org/10.1145/3511701openalex-snapshot

Testing the Plasticity of Reinforcement Learning-based Systems

inferred← citeshttps://doi.org/10.1145/3524610.3527899openalex-snapshot

Find bugs in static bug finders

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