SynthiCAD: Generation of Industrial Image Data Sets for Resilience Evaluation of Safety-Critical Classifiers
Berit Schuerrle, Venkatesh Sankarappan, Andrey Morozov · 2023
Due to their versatility, Deep Neural Networks are becoming increasingly relevant for the industrial domain.However, there are still challenges hindering their application, such as the lack of high-quality training data and suitable methods for assessing their robustness to internal computing hardware faults in safety-critical applications.To address these challenges, this paper introduces (i) a new data generation tool SynthiCAD for creating customisable image training data, along with an open-source industrial data set for classification generated by SynthiCAD.In addition, (ii) we categorized and compared existing approaches to fault injection and evaluated software-based fault injection using a VGG19 model trained on our new data set.Our findings show that software-based fault injection is a fast and scalable way to assess the reliability of DNNs under the presence of faults.