Identifying Adversarial Sentences by Analyzing Text Complexity

TL;DR

This paper proposes a method to detect adversarial text by analyzing differences in text complexity, coherence, and word usage, achieving higher accuracy than existing approaches.

Contribution

It introduces a novel feature-based approach leveraging text complexity and coherence to identify adversarial sentences, outperforming previous methods.

Findings

Achieved 82.0% accuracy in adversarial text detection.

Proved human-written text is more coherent and fluent.

Outperformed existing methods with higher accuracy and lower error rate.

Abstract

Attackers create adversarial text to deceive both human perception and the current AI systems to perform malicious purposes such as spam product reviews and fake political posts. We investigate the difference between the adversarial and the original text to prevent the risk. We prove that the text written by a human is more coherent and fluent. Moreover, the human can express the idea through the flexible text with modern words while a machine focuses on optimizing the generated text by the simple and common words. We also suggest a method to identify the adversarial text by extracting the features related to our findings. The proposed method achieves high performance with 82.0% of accuracy and 18.4% of equal error rate, which is better than the existing methods whose the best accuracy is 77.0% corresponding to the error rate 22.8%.