4 citations · 5 across the 4 of their papers we have counts for
4 papers
HyperNet-Adaptation for Diffusion-Based Test Case Generation
Oliver Weißl, Vincenzo Riccio, Severin Kacianka +1
The increasing deployment of deep learning systems requires systematic evaluation of their reliability in real-world scenarios. Traditional gradient-based adversarial attacks intro…
XMutant: XAI-based Fuzzing for Deep Learning Systems
Xingcheng Chen, Matteo Biagiola, Vincenzo Riccio +2
Semantic-based test generators are widely used to produce failure-inducing inputs for Deep Learning (DL) systems. They typically generate challenging test inputs by applying random…
Benchmarking Generative AI Models for Deep Learning Test Input Generation
Maryam, Matteo Biagiola, Andrea Stocco +1
Test Input Generators (TIGs) are crucial to assess the ability of Deep Learning (DL) image classifiers to provide correct predictions for inputs beyond their training and test sets…
Targeted Deep Learning System Boundary Testing
Oliver Weißl, Amr Abdellatif, Xingcheng Chen +4
Evaluating the behavioral boundaries of deep learning (DL) systems is crucial for understanding their reliability across diverse, unseen inputs. Existing solutions fall short as th…