Adversarial Robustness for Code

Jul 12, 2020



Machine learning and deep learning in particular has been recently used to successfully address many tasks in the domain of code including – finding and fixing bugs, code completion, decompilation, malware detection, type inference and many others. However, the issue of adversarial robustness of models for code has gone largely unnoticed. In this work we address this gap by: (i) developing adversarial attacks for code (a domain with discrete and highly structured inputs), (ii) showing that, similar to other domains, neural models for code are highly vulnerable to adversarial attacks, and (iii) developing a set of novel techniques that enable training robust and accurate models of code.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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