论文标题
对抗性攻击和文本的防御：一项调查
Adversarial Attacks and Defense on Texts: A Survey
论文作者
论文摘要
近年来，深度学习模型已被广泛用于各种目的，用于对象识别，自动驾驶汽车，面部识别，语音识别，情感分析等。但是，近年来，这些模型对噪音的虚弱迫使模型错误分类。这个问题在图像和音频域中进行了深入研究。关于文本数据的这个问题很少研究。已经对该主题进行了更少的调查，以了解不同类型的攻击和防御技术。在此手稿中，我们积累并分析了不同的进攻技术和各种防御模型，以提供更全面的想法。后来，我们指出了所有论文和挑战的一些有趣的发现，这些发现和挑战需要克服才能在这一领域前进。
Deep learning models have been used widely for various purposes in recent years in object recognition, self-driving cars, face recognition, speech recognition, sentiment analysis, and many others. However, in recent years it has been shown that these models possess weakness to noises which force the model to misclassify. This issue has been studied profoundly in the image and audio domain. Very little has been studied on this issue concerning textual data. Even less survey on this topic has been performed to understand different types of attacks and defense techniques. In this manuscript, we accumulated and analyzed different attacking techniques and various defense models to provide a more comprehensive idea. Later we point out some of the interesting findings of all papers and challenges that need to be overcome to move forward in this field.
