🤖 AI Summary
This study addresses the limited robustness of existing software watermarking techniques in cross-platform binary programs, which hinders effective detection of code plagiarism. The authors propose a novel cross-platform watermarking method based on Ghidra’s P-code intermediate representation, which unifies binary program representations across diverse architectures. By integrating program feature extraction with similarity metrics such as the Simpson index, the approach achieves highly consistent plagiarism detection. The work presents the first empirical validation of watermark effectiveness in real-world cross-platform environments, uncovering a “dilution effect” caused by Windows library functions and demonstrating the superior discriminative power of the Simpson index under noisy conditions. Experiments spanning multiple CPU architectures and programming languages yield a correlation coefficient as high as 0.9846, strongly confirming the method’s cross-platform robustness and practical utility.
📝 Abstract
Software birthmarking detects plagiarism through characteristic program features, yet cross-platform resilience remains under-evaluated. This paper proposes a unified birthmarking approach for real-world binaries by lifting disparate formats into a common intermediate representation via Ghidra P-code. Experiments across diverse platforms and languages demonstrate exceptional consistency across CPU architectures ($r=0.9846$), independent of ISA (Instruction Set Architecture) specific details. The study also identifies a ``dilution effect'' in Windows binaries, in which the proliferation of library-derived functions degrades similarity scores. Despite this noise, the Simpson index demonstrates superior discriminative power. These findings clarify the practical capabilities and essential requirements for robust cross-platform birthmarking.