Code datasets are of immense value for training neural-network-based code
completion models, where companies or organizations have made substantial
investments to establish and process these datasets. Unluckily, these datasets,
either built for proprietary or public usage, face the high risk of
unauthorized exploits, resulting from data leakages, license violations, etc.
Even worse, the ``black-box'' nature of neural models sets a high barrier for
externals to audit their training datasets, which further connives these
unauthorized usages. Currently, watermarking methods have been proposed to
prohibit inappropriate usage of image and natural language datasets. However,
due to domain specificity, they are not directly applicable to code datasets,
leaving the copyright protection of this emerging and important field of code
data still exposed to threats. To fill this gap, we propose a method, named
CodeMark, to embed user-defined imperceptible watermarks into code datasets to
trace their usage in training neural code completion models. CodeMark is based
on adaptive semantic-preserving transformations, which preserve the exact
functionality of the code data and keep the changes covert against
rule-breakers. We implement CodeMark in a toolkit and conduct an extensive
evaluation of code completion models. CodeMark is validated to fulfill all
desired properties of practical watermarks, including harmlessness to model
accuracy, verifiability, robustness, and imperceptibility.

应对代码数据集的版权保护问题，我们提出了一种名为 CodeMark 的方法，通过自适应的语义保持转换，将用户定义的隐形水印嵌入到代码数据集中，以追踪其在训练神经网络代码完成模型中的使用。CodeMark 工具包经过广泛评估，被验证具有实际水印所需的全部属性，包括对模型准确性无害、可验证性、鲁棒性和不可察觉性。