Exploring Security Vulnerabilities of Deep Learning Models by Adversarial Attacks

Abstract

Nowadays, deep learning models play an important role in a variety of scenarios, such as image classification, natural language processing, and speech recognition. However, deep learning models are shown to be vulnerable; a small change to the original data may affect the output of the model, which may incur severe consequences such as misrecognition and privacy leakage. The intentionally modified data is referred to as adversarial examples. In this paper, we explore the security vulnerabilities of deep learning models designed for textual analysis. Specifically, we propose a visual similar word replacement (VSWR) algorithm to generate adversarial examples against textual analysis models. By using adversarial examples as the input of deep learning models, we verified that deep learning models are vulnerable to such adversarial attacks. We have conducted experiments on several sentiment analysis deep learning models to evaluate the performance. The results also confirmed that the generated adversarial examples could successfully attack deep learning models. As the number of modified words increases, the model prediction accuracy becomes lower. This kind of adversarial attack implies security vulnerabilities of deep learning models.