Peixin Zhang

Orcid: 0000-0001-5039-5651

Affiliations:

Singapore Management University, Singapore

According to our database¹, Peixin Zhang authored at least 24 papers between 2018 and 2026.

Collaborative distances:

Dijkstra number² of three.
Erdős number³ of four.

Timeline

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Book In proceedings Article PhD thesis Dataset Other

Bibliography

2026

ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection.

[BibT_eX]

[DOI]

CoRR, April, 2026

PRUNE: A patching based repair framework for certifiable and privacy-robust unlearning of neural networks.

[BibT_eX]

[DOI]

Neural Networks, 2026

Be Responsible in Your Answers! Monitoring Out-of-Domain Behaviors in Domain-Specific LLMs.

[BibT_eX]

[DOI]

Proceedings of the ACM Web Conference 2026, 2026

LLMQuA: Practical Backdoor Injection on Large Language Model Quantization.

[BibT_eX]

[DOI]

Proceedings of the ACM Web Conference 2026, 2026

Rounding-Guided Backdoor Injection in Deep Learning Model Quantization.

[BibT_eX]

[DOI]

Proceedings of the 33rd Annual Network and Distributed System Security Symposium, 2026

Towards Provably Unlearnable Examples via Bayes Error Optimization.

[BibT_eX]

[DOI]

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Towards Provably Unlearnable Examples via Bayes Error Optimisation.

[BibT_eX]

[DOI]

CoRR, November, 2025

PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks.

[BibT_eX]

[DOI]

CoRR, May, 2025

LLMScan: Causal Scan for LLM Misbehavior Detection.

[BibT_eX]

[DOI]

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively.

[BibT_eX]

[DOI]

CoRR, 2024

LLMScan: Causal Scan for LLM Misbehavior Detection.

[BibT_eX]

[DOI]

CoRR, 2024

Resilient Watermarking for LLM-Generated Codes.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Boosting Adversarial Training in Safety-Critical Systems Through Boundary Data Selection.

[BibT_eX]

[DOI]

Yifan Jia

Christopher M. Poskitt

Peixin Zhang

Jingyi Wang

Jun Sun

Sudipta Chattopadhyay

IEEE Robotics Autom. Lett., December, 2023

QuoTe: Quality-oriented Testing for Deep Learning Systems.

[BibT_eX]

[DOI]

ACM Trans. Softw. Eng. Methodol., September, 2023

Backdoor Attack through Machine Unlearning.

[BibT_eX]

[DOI]

CoRR, 2023

Towards Certified Probabilistic Robustness with High Accuracy.

[BibT_eX]

[DOI]

Ruihan Zhang

Peixin Zhang

Jun Sun

CoRR, 2023

2022

Automatic Fairness Testing of Neural Classifiers Through Adversarial Sampling.

[BibT_eX]

[DOI]

IEEE Trans. Software Eng., 2022

2021

Fairness Testing of Deep Image Classification with Adequacy Metrics.

[BibT_eX]

[DOI]

CoRR, 2021

2020

White-box fairness testing through adversarial sampling.

[BibT_eX]

[DOI]

Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June, 2020

An Empirical Study on Correlation between Coverage and Robustness for Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 25th International Conference on Engineering of Complex Computer Systems, 2020

2019

There is Limited Correlation between Coverage and Robustness for Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

Adversarial sample detection for deep neural network through model mutation testing.

[BibT_eX]

[DOI]

Proceedings of the 41st International Conference on Software Engineering, 2019

2018

Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing.

[BibT_eX]

[DOI]

CoRR, 2018

Towards optimal concolic testing.

[BibT_eX]

[DOI]

Proceedings of the 40th International Conference on Software Engineering, 2018

Peixin Zhang

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