Decomposition then watermarking: Enhancing code traceability with dual-channel code watermarking

IF 3.1 2区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Haibo Lin, Zhong Li, Ruihua Ji, Minxue Pan, Tian Zhang, Nan Wu, Xuandong Li
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引用次数: 0

Abstract

Code watermarking has gained increasing attention for tracing the provenance of code with the rapid growth of the open-source community. Existing work on code watermarking has shown promising results yet still falls short, especially when a multi-bit watermark for encoding diverse information is required. In this paper, we propose DWC, a novel code watermarking method with highly watermark capacity. The key idea of DWC is to first decompose the code into natural and formal channels, then embed the watermark separately into each channel based solely on its respective information. As such, DWC reduces the mutual interference between these two channels and the impacts of irrelevant information within the code, thus enabling more effective transformations for embedding watermarks with higher capacity and robustness. Our extensive experiments on source code snippets in four programming languages (C, C++, Java, and Python) demonstrate the effectiveness, efficiency, and capability of DWC in embedding multi-bit watermarks, as well as the utility and robustness of the watermarked code it generates.

分解后加水印:利用双通道码水印增强码的可追溯性
随着开源社区的快速发展,代码水印技术在追踪代码来源方面受到越来越多的关注。现有的编码水印研究已经取得了可喜的成果,但仍然存在不足,特别是当需要一个多比特的水印来编码多种信息时。本文提出了一种具有高水印容量的新型编码水印方法DWC。DWC的关键思想是首先将代码分解为自然信道和形式信道,然后仅根据各自的信息将水印分别嵌入到每个信道中。因此,DWC减少了这两个信道之间的相互干扰和代码中不相关信息的影响,从而实现更有效的转换,以更高的容量和鲁棒性嵌入水印。我们对四种编程语言(C、c++、Java和Python)的源代码片段进行了广泛的实验,证明了DWC在嵌入多比特水印方面的有效性、效率和能力,以及它生成的水印代码的实用性和鲁棒性。
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来源期刊
Automated Software Engineering
Automated Software Engineering 工程技术-计算机:软件工程
CiteScore
4.80
自引率
11.80%
发文量
51
审稿时长
>12 weeks
期刊介绍: This journal details research, tutorial papers, survey and accounts of significant industrial experience in the foundations, techniques, tools and applications of automated software engineering technology. This includes the study of techniques for constructing, understanding, adapting, and modeling software artifacts and processes. Coverage in Automated Software Engineering examines both automatic systems and collaborative systems as well as computational models of human software engineering activities. In addition, it presents knowledge representations and artificial intelligence techniques applicable to automated software engineering, and formal techniques that support or provide theoretical foundations. The journal also includes reviews of books, software, conferences and workshops.
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