Liu, Peng

Semantics-based obfuscation-resilient binary code similarity comparison with applications to software plagiarism detection

Luo, L., Ming, J., Wu, D., Liu, P., & Zhu, S. (2014) · Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

Software Engineering ResearchAcademic integrity and plagiarism

Cited by 220

Semantics-Based Obfuscation-Resilient Binary Code Similarity Comparison with Applications to Software and Algorithm Plagiarism Detection

Luo, L., Ming, J., Wu, D., Liu, P., & Zhu, S. (2017) · IEEE Transactions on Software Engineering

Software Engineering ResearchSecurity and Verification in Computing

Cited by 144

Behavior based software theft detection

Wang, X., Jhi, Y.-C., Zhu, S., & Liu, P. (2009) · Proceedings of the 16th ACM conference on Computer and communications security

Software Engineering ResearchPhysical Unclonable Functions (PUFs) and Hardware Security

Cited by 101

Value-based program characterization and its application to software plagiarism detection

Jhi, Y.-C., Wang, X., Jia, X., Zhu, S., Liu, P., & Wu, D. (2011) · Proceedings of the 33rd International Conference on Software Engineering

Software Engineering ResearchAcademic integrity and plagiarism

Cited by 83

Program Characterization Using Runtime Values and Its Application to Software Plagiarism Detection

Jhi, Y.-C., Jia, X., Wang, X., Zhu, S., Liu, P., & Wu, D. (2015) · IEEE Transactions on Software Engineering

Software Engineering ResearchSecurity and Verification in Computing

Cited by 35

Deviation-Based Obfuscation-Resilient Program Equivalence Checking With Application to Software Plagiarism Detection

Ming, J., Zhang, F., Wu, D., Liu, P., & Zhu, S. (2016) · IEEE Transactions on Reliability

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 33

To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt

Wang, Z., Nagaraja, N., Zhang, L., Bahsi, H., Patil, P., & Liu, P. (2025) · 2025 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 13