Papers DNN Testing
“DNN Testing” 태그가 달린 논문 27편 · 필터 해제
RBT4DNN: Requirements-based Testing of Neural Networks
Deep neural network (DNN) testing is crucial for the reliability and safety of critical systems, where failures can have severe consequences. Although various techniques have been developed to create robustness test suit…
DNN TestingGAN-enhanced Simulation-driven DNN Testing in Absence of Ground Truth
The generation of synthetic inputs via simulators driven by search algorithms is essential for cost-effective testing of Deep Neural Network (DNN) components for safety-critical systems. However, in many applications, si…
DNN TestingDANDI: Diffusion as Normative Distribution for Deep Neural Network Input
Surprise Adequacy (SA) has been widely studied as a test adequacy metric that can effectively guide software engineers towards inputs that are more likely to reveal unexpected behaviour of Deep Neural Networks (DNNs). In…
DNN TestingBridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems
Deep Neural Networks (DNNs) for Autonomous Driving Systems (ADS) are typically trained on real-world images and tested using synthetic simulator images. This approach results in training and test datasets with dissimilar…
Autonomous DrivingDiversityDNN Testingobject-detection+2Targeted Deep Learning System Boundary Testing
Evaluating the behavioral boundaries of deep learning (DL) systems is crucial for understanding their reliability across diverse, unseen inputs. Existing solutions fall short as they rely on untargeted random, model- or …
Deep LearningDNN Testingimage-classificationImage ClassificationDeepSample: DNN sampling-based testing for operational accuracy assessment
Deep Neural Networks (DNN) are core components for classification and regression tasks of many software systems. Companies incur in high costs for testing DNN with datasets representative of the inputs expected in operat…
DNN TestingregressionDeepKnowledge: Generalisation-Driven Deep Learning Testing
Despite their unprecedented success, DNNs are notoriously fragile to small shifts in data distribution, demanding effective testing techniques that can assess their dependability. Despite recent advances in DNN testing, …
Deep LearningDNN TestingRethinking Diversity in Deep Neural Network Testing
Motivated by the success of traditional software testing, numerous diversity measures have been proposed for testing deep neural networks (DNNs). In this study, we propose a shift in perspective, advocating for the consi…
DiversityDNN Testingsoftware testingCGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks
In this paper, we propose a new Deep Neural Network (DNN) testing algorithm called the Constrained Gradient Descent (CGD) method, and an implementation we call CGDTest aimed at exposing security and robustness issues suc…
Adversarial RobustnessDNN TestingAn Overview of Structural Coverage Metrics for Testing Neural Networks
Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overvie…
DNN TestingNPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks
Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural Networks (DNNs) still raises concerns in…
Defect DetectionDNN Testingimage-classificationImage Classification+1CatchBackdoor: Backdoor Detection via Critical Trojan Neural Path Fuzzing
The success of deep neural networks (DNNs) in real-world applications has benefited from abundant pre-trained models. However, the backdoored pre-trained models can pose a significant trojan threat to the deployment of d…
DNN TestingBlack-Box Testing of Deep Neural Networks Through Test Case Diversity
Deep Neural Networks (DNNs) have been extensively used in many areas including image processing, medical diagnostics, and autonomous driving. However, DNNs can exhibit erroneous behaviours that may lead to critical error…
Autonomous DrivingDiversityDNN TestingFault DetectionRevisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion
Various deep neural network (DNN) coverage criteria have been proposed to assess DNN test inputs and steer input mutations. The coverage is characterized via neurons having certain outputs, or the discrepancy between neu…
DiversityDNN TestingProvably Valid and Diverse Mutations of Real-World Media Data for DNN Testing
Deep neural networks (DNNs) often accept high-dimensional media data (e.g., photos, text, and audio) and understand their perceptual content (e.g., a cat). To test DNNs, diverse inputs are needed to trigger mis-predictio…
DNN TestingvalidPerception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing
Visual question answering (VQA) takes an image and a natural-language question as input and returns a natural-language answer. To date, VQA models are primarily assessed by their accuracy on high-level reasoning ques…
BenchmarkingDNN TestingQuestion AnsweringVisual Question Answering+1Exposing Previously Undetectable Faults in Deep Neural Networks
Existing methods for testing DNNs solve the oracle problem by constraining the raw features (e.g. image pixel values) to be within a small distance of a dataset example for which the desired DNN output is known. But this…
DNN TestingNeuron Coverage-Guided Domain Generalization
This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent pro…
DNN TestingDomain GeneralizationDistribution-Aware Testing of Neural Networks Using Generative Models
The reliability of software that has a Deep Neural Network (DNN) as a component is urgently important today given the increasing number of critical applications being deployed with DNNs. The need for reliability raises a…
DNN TestingvalidIWA: Integrated Gradient based White-box Attacks for Fooling Deep Neural Networks
The widespread application of deep neural network (DNN) techniques is being challenged by adversarial examples, the legitimate input added with imperceptible and well-designed perturbations that can fool DNNs easily in t…
DNN Testing