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- W3034846000 abstract "Machine learning and deep learning in particular has been recently used to successfully address many tasks in the domain of code such as finding and fixing bugs, code completion, decompilation, type inference and many others. However, the issue of adversarial robustness of models for code has gone largely unnoticed. In this work, we explore this issue by: (i) instantiating adversarial attacks for code (a domain with discrete and highly structured inputs), (ii) showing that, similar to other domains, neural models for code are vulnerable to adversarial attacks, and (iii) combining existing and novel techniques to improve robustness while preserving high accuracy." @default.
- W3034846000 created "2020-06-19" @default.
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- W3034846000 date "2020-07-12" @default.
- W3034846000 modified "2023-09-23" @default.
- W3034846000 title "Adversarial Robustness for Code" @default.
- W3034846000 doi "https://doi.org/10.3929/ethz-b-000466229" @default.
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