文本的对抗攻击与防御研究综述
Adversarial Attacks and Defense on Texts: A Survey
摘要：
近年来，深度学习模型已被广泛用于物体识别、自动驾驶汽车、人脸识别、语音识别、情感分析等各种目的。然而，近年来的研究表明，这些模型对噪声的敏感性较差，从而导致模型的误分类。这个问题在图像和音频领域得到了深入的研究。关于文本数据的这一问题研究很少。甚至更少的调查已经执行了这个主题，以了解不同类型的攻击和防御技术。在本文中，我们积累和分析了不同的攻击技术和各种防御模型，以提供更全面的想法。随后，我们指出了所有论文中的一些有趣的发现以及在这一领域取得进展需要克服的挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
