SE4SafeML 2023 – Preliminary Table of Contents

1st International Workshop on Dependability and Trustworthiness of Safety-Critical Systems with Machine Learned Components (SE4SafeML 2023)

Frontmatter

Papers

Mutation-Based Analysis of Test Effectiveness Metrics for Simulation-Based Testing of DNN-Enabled Cyber-Physical Systems
Eunho Cho and In-Young Ko
(KAIST, South Korea)

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Rule-Based Testing of Neural Networks
Muhammad Usman , Youcheng Sun , Divya Gopinath , and Corina S. Păsăreanu 
(University of Texas, USA; University of Manchester, UK; KBR @ NASA Ames Research Center, USA; Carnegie Mellon University, USA)

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GNNSlicer: Protecting GNN Models against Injection Attacks with Model Slicing
Ziqi Zhang , Jintang Li , Yifeng Cai , Ding Li , and Yao Guo 
(Peking University, China; Sun Yat-sen University, China)

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FedDefender: Backdoor Attack Defense in Federated Learning
Waris Gill , Ali Anwar , and Muhammad Ali Gulzar 
(Virginia Tech, USA; University of Minnesota, USA)

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MLGuard: Defend Your Machine Learning Model!
Sheng Wong , Scott Barnett , Jessica Rivera-Villicana , Anj Simmons , Hala Abdelkader , Jean-Guy Schneider , and Rajesh Vasa 
(Deakin University, Australia; RMIT University, Australia; Monash University, Australia)

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Interpretable On-the-Fly Repair of Deep Neural Classifiers
Hossein Mohasel Arjomandi  and Reyhaneh Jabbarvand 
(University of Illinois at Urbana-Champaign, USA)

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Towards Safe ML-Based Systems in Presence of Feedback Loops
Sumon Biswas , Yining She , and Eunsuk Kang 
(Carnegie Mellon University, USA)

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The Case for Scalable Quantitative Neural Network Analysis
Mara Downing  and Tevfik Bultan 
(University of California at Santa Barbara, USA)

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