论文标题
FastWordBug：一种针对NLP应用程序生成对抗文本的快速方法
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications
论文作者
论文摘要
在本文中，我们提出了一种新颖的算法，即FastWordbug，以有效地在黑色盒子设置中生成小文本扰动，以迫使情感分析或文本分类模式来做出错误的预测。通过结合单词语音属性的一部分，我们提出了一种评分方法，该方法可以快速识别影响文本分类的重要单词。我们在三个真实世界文本数据集和两个最先进的机器学习模型上评估了FastWordbug。结果表明，我们的方法可以显着降低模型的准确性，同时，我们可以以最高的攻击效率将模型称为模型。我们还攻击了NLP的两种流行的现实世界云服务，结果表明我们的方法也有效。
In this paper, we present a novel algorithm, FastWordBug, to efficiently generate small text perturbations in a black-box setting that forces a sentiment analysis or text classification mode to make an incorrect prediction. By combining the part of speech attributes of words, we propose a scoring method that can quickly identify important words that affect text classification. We evaluate FastWordBug on three real-world text datasets and two state-of-the-art machine learning models under black-box setting. The results show that our method can significantly reduce the accuracy of the model, and at the same time, we can call the model as little as possible, with the highest attack efficiency. We also attack two popular real-world cloud services of NLP, and the results show that our method works as well.
