173 citations · 397 across the 28 of their papers we have counts for
7 papers · 1 filter
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Jialuo Chen, Jingyi Wang, Tinglan Peng +6
Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauth…
Tutorials on Testing Neural Networks
Nicolas Berthier, Youcheng Sun, Wei Huang +3
Deep learning achieves remarkable performance on pattern recognition, but can be vulnerable to defects of some important properties such as robustness and security. This tutorial i…
NNrepair: Constraint-based Repair of Neural Network Classifiers
Muhammad Usman, Divya Gopinath, Youcheng Sun +2
We present NNrepair, a constraint-based technique for repairing neural network classifiers. The technique aims to fix the logic of the network at an intermediate layer or at the la…
Explanations for Occluded Images
Hana Chockler, Daniel Kroening, Youcheng Sun
Existing algorithms for explaining the output of image classifiers perform poorly on inputs where the object of interest is partially occluded. We present a novel, black-box algori…
NEUROSPF: A tool for the Symbolic Analysis of Neural Networks
Muhammad Usman, Yannic Noller, Corina Pasareanu +2
This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and…
RobOT: Robustness-Oriented Testing for Deep Learning Systems
Jingyi Wang, Jialuo Chen, Youcheng Sun +4
Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction…
Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning
Renjue Li, Pengfei Yang, Cheng-Chao Huang +3
To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with sc…