The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Unlearnable Examples: Protecting Open-Source Software from Unauthorized Neural Code Learning
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In the citing paper
“Ji, Ma & Wang (2022) developed a method to protect open-source code by applying lightweight transformations.”
From Generative AI and intellectual property: a systematic literature review of possible issues and mitigations (2026) · cites this paper as Ji, Ma & Wang, 2022
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Citations
Cites
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Evaluating Large Language Models Trained on Code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436
Evaluating clone detection tools with BigCloneBench
Svajlenko, J., & Roy, C. K. (2015) · 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME) · doi:10.1109/icsm.2015.7332459 · Cited by 172
Research works printed but not linked: 16
- Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. code2seq: Generating sequences from structured representations of code. ICLR.
- Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John Dickerson, Gavin Taylor, and Tom Goldstein. LowKey: leveraging adversarial attacks to protect social media users from facial recognition. arXiv preprint arXiv:2101.07922, 2021. · arXiv
- Cynthia Dwork. Differential privacy. In International Colloquium on Automata, Languages, and Programming, pages 1-12. Springer, 2006.
- Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al. Code- BERT: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155, 2020. · arXiv
- Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang. Unlearnable examples: Making personal data unex- ploitable. arXiv preprint arXiv:2101.04898, 2021. · arXiv
- Chen Liang, Jonathan Berant, Quoc Le, Kenneth D Forbus, and Ni Lao. Neural symbolic machines: Learning semantic parsers on freebase with weak supervision. In ACL, 2017.
- Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al. Codexglue: A machine learning benchmark dataset for code understanding and generation. arXiv preprint arXiv:2102.04664, 2021. · arXiv
- George A Miller. WordNet: a lexical database for english. Communi- cations of the ACM, 1995.
- Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. Convolutional neural networks over tree structures for programming language processing. In AAAI, 2016.
- Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan- Gavitt, and Ramesh Karri. An empirical cybersecurity evaluation of github copilot's code contributions. arXiv preprint arXiv:2108.09293. · arXiv
- Lutz Prechelt, Guido Malpohl, Michael Philippsen, et al. Finding plagiarisms among a set of programs with JPlag. J. UCS, 8(11):1016.
- Erwin Quiring, Alwin Maier, and Konrad Rieck. Misleading authorship attribution of source code using adversarial learning. In USENIX Security, 2019.
- Saul Schleimer, Daniel S Wilkerson, and Alex Aiken. Winnowing: local algorithms for document fingerprinting. In SIGMOD, 2003.
- Zhensu Sun, Xiaoning Du, Fu Song, Mingze Ni, and Li Li. Coprotector: Protect open-source code against unauthorized training usage with data poisoning. arXiv preprint arXiv:2110.12925, 2021. · arXiv
- Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, and Hui Xue. Towards face encryption by generating adversarial identity masks. In ICCV, 2021.
- Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu. Devign: Effective vulnerability identification by learning compre- hensive program semantics via graph neural networks. arXiv preprint arXiv:1909.03496, 2019. · arXiv
Cited by
Arnaudo, A., Coppola, R., Morisio, M., Vetrò, A., Borghi, M., Raso, R., & Khan, B. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3928 · Cited by 2
Metadata
- Authors
- Ji, Z., Ma, P., & Wang, S.
- Venue
- International Conferences on Software Engineering and Knowledge Engineering
- Publisher
- KSI Research Inc.
- Published
- 2022-07-01
- Volume
- 2022
- Pages
- 525-530
- ISSN
- 2325-9000
- Cited by
- 5
- References
- 3
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
