# TextBugger: Generating Adversarial Text Against Real-world Applications

## TL;DR

TextBugger is a novel adversarial attack framework that effectively and efficiently generates human-preserving texts to fool deep learning-based text understanding systems, exposing security vulnerabilities in real-world applications.

## Contribution

The paper introduces TextBugger, a new attack method that outperforms existing techniques in success rate, preserves text utility, and operates with sub-linear complexity relative to text length.

## Key findings

- Achieves 100% success rate on sentiment analysis systems
- Preserves 97% semantic similarity with original texts
- Generates adversarial texts within seconds

## Abstract

Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification. Despite its tremendous popularity, the security vulnerabilities of DLTU are still largely unknown, which is highly concerning given its increasing use in security-sensitive applications such as sentiment analysis and toxic content detection. In this paper, we show that DLTU is inherently vulnerable to adversarial text attacks, in which maliciously crafted texts trigger target DLTU systems and services to misbehave. Specifically, we present TextBugger, a general attack framework for generating adversarial texts. In contrast to prior works, TextBugger differs in significant ways: (i) effective -- it outperforms state-of-the-art attacks in terms of attack success rate; (ii) evasive -- it preserves the utility of benign text, with 94.9\% of the adversarial text correctly recognized by human readers; and (iii) efficient -- it generates adversarial text with computational complexity sub-linear to the text length. We empirically evaluate TextBugger on a set of real-world DLTU systems and services used for sentiment analysis and toxic content detection, demonstrating its effectiveness, evasiveness, and efficiency. For instance, TextBugger achieves 100\% success rate on the IMDB dataset based on Amazon AWS Comprehend within 4.61 seconds and preserves 97\% semantic similarity. We further discuss possible defense mechanisms to mitigate such attack and the adversary's potential countermeasures, which leads to promising directions for further research.