An Image is Worth a Thousand Toxic Words: A Metamorphic Testing Framework for Content Moderation Software

TL;DR

This paper introduces OASIS, a metamorphic testing framework that generates toxic image test cases to evaluate and improve the robustness of content moderation software against evasion tactics.

Contribution

OASIS is the first metamorphic testing framework specifically designed for content moderation, leveraging real-world toxic content to identify vulnerabilities in commercial and research models.

Findings

OASIS achieves up to 100% error detection rate on tested models.

Retraining with OASIS-generated data enhances model robustness.

The framework uncovers significant vulnerabilities in current moderation tools.

Abstract

The exponential growth of social media platforms has brought about a revolution in communication and content dissemination in human society. Nevertheless, these platforms are being increasingly misused to spread toxic content, including hate speech, malicious advertising, and pornography, leading to severe negative consequences such as harm to teenagers' mental health. Despite tremendous efforts in developing and deploying textual and image content moderation methods, malicious users can evade moderation by embedding texts into images, such as screenshots of the text, usually with some interference. We find that modern content moderation software's performance against such malicious inputs remains underexplored. In this work, we propose OASIS, a metamorphic testing framework for content moderation software. OASIS employs 21 transform rules summarized from our pilot study on 5,000 real-world toxic contents collected from 4 popular social media applications, including Twitter, Instagram, Sina Weibo, and Baidu Tieba. Given toxic textual contents, OASIS can generate image test cases, which preserve the toxicity yet are likely to bypass moderation. In the evaluation, we employ OASIS to test five commercial textual content moderation software from famous companies (i.e., Google Cloud, Microsoft Azure, Baidu Cloud, Alibaba Cloud and Tencent Cloud), as well as a state-of-the-art moderation research model. The results show that OASIS achieves up to 100% error finding rates. Moreover, through retraining the models with the test cases generated by OASIS, the robustness of the moderation model can be improved without performance degradation.