论文标题
源代码模型的语义鲁棒性
Semantic Robustness of Models of Source Code
论文作者
论文摘要
深度神经网络容易受到对抗性示例的攻击 - 导致预测不正确的小输入扰动。我们研究了源代码模型的此问题，我们希望网络在保留代码功能的源代码修改方面具有鲁棒性。 （1）我们定义了一个强大的对手，该对手可以采用参数，语义保护程序转换的序列； （2）我们展示了如何进行对抗性训练，以学习对这种对手的强大模型； （3）我们对不同语言和体系结构进行评估，表明鲁棒性的定量增长很大。
Deep neural networks are vulnerable to adversarial examples - small input perturbations that result in incorrect predictions. We study this problem for models of source code, where we want the network to be robust to source-code modifications that preserve code functionality. (1) We define a powerful adversary that can employ sequences of parametric, semantics-preserving program transformations; (2) we show how to perform adversarial training to learn models robust to such adversaries; (3) we conduct an evaluation on different languages and architectures, demonstrating significant quantitative gains in robustness.
