Untargeted Code Authorship Evasion with Seq2Seq Transformation
Soohyeon Choi Rhongho Jang DaeHun Nyang David Mohaisen
Code authorship attribution is the problem of identifying authors of
programming language codes through the stylistic features in their codes, a
topic that recently witnessed significant interest with outstanding
performance. In this work, we present SCAE, a code authorship obfuscation
technique that leverages a Seq2Seq code transformer called StructCoder. SCAE
customizes StructCoder, a system designed initially for function-level code
translation from one language to another (e.g., Java to C#), using transfer
learning. SCAE improved the efficiency at a slight accuracy degradation
compared to existing work. We also reduced the processing time by about 68%
while maintaining an 85% transformation success rate and up to 95.77% evasion
success rate in the untargeted setting.