5 citations · 9 across the 6 of their papers we have counts for
8 papers
Prophecy: Inferring Formal Properties from Neuron Activations
Divya Gopinath, Corina S. Pasareanu, Muhammad Usman
We present Prophecy, a tool for automatically inferring formal properties of feed-forward neural networks. Prophecy is based on the observation that a significant part of the logic…
An Overview of Structural Coverage Metrics for Testing Neural Networks
Muhammad Usman, Youcheng Sun, Divya Gopinath +3
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…
VPN: Verification of Poisoning in Neural Networks
Youcheng Sun, Muhammad Usman, Divya Gopinath +1
Neural networks are successfully used in a variety of applications, many of them having safety and security concerns. As a result researchers have proposed formal verification tech…
AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks
Muhammad Usman, Youcheng Sun, Divya Gopinath +1
We study backdoor poisoning attacks against image classification networks, whereby an attacker inserts a trigger into a subset of the training data, in such a way that at test time…
QuantifyML: How Good is my Machine Learning Model?
Muhammad Usman, Divya Gopinath, Corina S. Păsăreanu
The efficacy of machine learning models is typically determined by computing their accuracy on test data sets. However, this may often be misleading, since the test data may not be…
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…