Adversarial Product Review Generation with Word Replacements

Machine learning algorithms including Deep Neural Networks (DNNs) have shown great success in many different areas. However, they are frequently susceptible to adversarial examples, which are maliciously crafted inputs to fool machine learning classifiers. On the other hand, humans cannot distinguish between non-adversarial and adversarial inputs. In this work, we focus on creating adversarial examples to change the polarity of positive and negative reviews with Amazon product review dataset. We introduce a simple heuristics algorithm to construct adversarial product reviews by replacing words with semantically and synthetically similar synonyms. We evaluate our approach against the state-of-the-art CNN-BLSTM classifier. Our preliminary results show the performance drop of the classifier against the adversarial examples. We also present the defense mechanism using adversarial training.