22 citations · 96 across the 18 of their papers we have counts for
5 papers · 1 filter
Scaling Model Checking for DNN Analysis via State-Space Reduction and Input Segmentation (Extended Version)
Mahum Naseer, Osman Hasan, Muhammad Shafique
Owing to their remarkable learning capabilities and performance in real-world applications, the use of machine learning systems based on Neural Networks (NNs) has been continuously…
Poster: Link between Bias, Node Sensitivity and Long-Tail Distribution in trained DNNs
Mahum Naseer, Muhammad Shafique
Owing to their remarkable learning (and relearning) capabilities, deep neural networks (DNNs) find use in numerous real-world applications. However, the learning of these data-driv…
UnbiasedNets: A Dataset Diversification Framework for Robustness Bias Alleviation in Neural Networks
Mahum Naseer, Bharath Srinivas Prabakaran, Osman Hasan +1
Performance of trained neural network (NN) models, in terms of testing accuracy, has improved remarkably over the past several years, especially with the advent of deep learning. H…
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient
Farzad Nikfam, Alberto Marchisio, Maurizio Martina +1
Adversarial training is exploited to develop a robust Deep Neural Network (DNN) model against the malicious altered data. These attacks may have catastrophic effects on DNN models…
fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems
Alberto Marchisio, Giovanni Caramia, Maurizio Martina +1
Recently, Deep Neural Networks (DNNs) have achieved remarkable performances in many applications, while several studies have enhanced their vulnerabilities to malicious attacks. In…