# SHIELD: Thwarting Code Authorship Attribution

arXiv: https://arxiv.org/abs/2304.13255 · HTML: https://mlsanity.com/paper/2304.13255/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Misleading Authorship Attribution of Source Code using Adversarial Learning](https://mlsanity.com/paper/1905.12386/) (2019)
- [RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation](https://mlsanity.com/paper/2202.06043/) (2022)
- [When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries](https://mlsanity.com/paper/1512.08546/) (2015)
- [Defeating Misclassification Attacks Against Transfer Learning](https://mlsanity.com/paper/1908.11230/) (2019)
- [Adversarial Binaries for Authorship Identification](https://mlsanity.com/paper/1809.08316/) (2018)

## Built on by (most cited first, 2 of 2)

- [Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution](https://mlsanity.com/paper/2508.15840/) (2025)
- [Stylometric Defenses Against Author Impersonation in Software Repositories](https://mlsanity.com/paper/2608.02695/) (2026)

## Abstract (arXiv)

Authorship attribution has become increasingly accurate, posing a serious privacy risk for programmers who wish to remain anonymous. In this paper, we introduce SHIELD to examine the robustness of different code authorship attribution approaches against adversarial code examples. We define four attacks on attribution techniques, which include targeted and non-targeted attacks, and realize them using adversarial code perturbation. We experiment with a dataset of 200 programmers from the Google Code Jam competition to validate our methods targeting six state-of-the-art authorship attribution methods that adopt a variety of techniques for extracting authorship traits from source-code, including RNN, CNN, and code stylometry. Our experiments demonstrate the vulnerability of current authorship attribution methods against adversarial attacks. For the non-targeted attack, our experiments demonstrate the vulnerability of current authorship attribution methods against the attack with an attack success rate exceeds 98.5% accompanied by a degradation of the identification confidence that exceeds 13%. For the targeted attacks, we show the possibility of impersonating a programmer using targeted-adversarial perturbations with a success rate ranging from 66% to 88% for different authorship attribution techniques under several adversarial scenarios.