173 citations · 397 across the 28 of their papers we have counts for
29 papers
QNNRepair: Quantized Neural Network Repair
Xidan Song, Youcheng Sun, Mustafa A. Mustafa +1
We present QNNRepair, the first method in the literature for repairing quantized neural networks (QNNs). QNNRepair aims to improve the accuracy of a neural network model after quan…
A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification
Norbert Tihanyi, Ridhi Jain, Yiannis Charalambous +3
This paper introduces an innovative approach that combines Large Language Models (LLMs) with Formal Verification strategies for automatic software vulnerability repair. Initially,…
Safety Analysis of Autonomous Driving Systems Based on Model Learning
Renjue Li, Tianhang Qin, Pengfei Yang +3
We present a practical verification method for safety analysis of the autonomous driving system (ADS). The main idea is to build a surrogate model that quantitatively depicts the b…
AIREPAIR: A Repair Platform for Neural Networks
Xidan Song, Youcheng Sun, Mustafa A. Mustafa +1
We present AIREPAIR, a platform for repairing neural networks. It features the integration of existing network repair tools. Based on AIREPAIR, one can run different repair methods…
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…
VeriFi: Towards Verifiable Federated Unlearning
Xiangshan Gao, Xingjun Ma, Jingyi Wang +5
Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is…
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…
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…
Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems
Wei Huang, Yifan Zhou, Gaojie Jin +4
This paper presents a formal verification guided approach for a principled design and implementation of robust and resilient learning-enabled systems. We focus on learning-enabled…
Ranking Policy Decisions
Hadrien Pouget, Hana Chockler, Youcheng Sun +1
Policies trained via Reinforcement Learning (RL) are often needlessly complex, making them difficult to analyse and interpret. In a run with time steps, a policy will make …
Reliability Validation of Learning Enabled Vehicle Tracking
Youcheng Sun, Yifan Zhou, Simon Maskell +2
This paper studies the reliability of a real-world learning-enabled system, which conducts dynamic vehicle tracking based on a high-resolution wide-area motion imagery input. The s…
Coverage Guided Testing for Recurrent Neural Networks
Wei Huang, Youcheng Sun, Xingyu Zhao +4
Recurrent neural networks (RNNs) have been applied to a broad range of applications, including natural language processing, drug discovery, and video recognition. Their vulnerabili…
Explaining Image Classifiers using Statistical Fault Localization
Youcheng Sun, Hana Chockler, Xiaowei Huang +1
The black-box nature of deep neural networks (DNNs) makes it impossible to understand why a particular output is produced, creating demand for "Explainable AI". In this paper, we s…
testRNN: Coverage-guided Testing on Recurrent Neural Networks
Wei Huang, Youcheng Sun, Xiaowei Huang +1
Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction. We introduce th…
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang, Daniel Kroening, Wenjie Ruan +5
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deplo…
Concolic Testing for Deep Neural Networks
Youcheng Sun, Min Wu, Wenjie Ruan +3
Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for…
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Norm
Wenjie Ruan, Min Wu, Youcheng Sun +3
Deployment of deep neural networks (DNNs) in safety- or security-critical systems requires provable guarantees on their correct behaviour. A common requirement is robustness to adv…