你看到我所看到的吗？自动化多媒体内容分析的能力和局限性
Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysis
摘要：
近年来，不断增加的用户生成的在线内容导致了对自动内容分析工具的研究和投资的扩大。2019冠状病毒病疫情期间，对自动化内容分析的审查加快，原因是社交网络服务因担心其审核人员因亲自工作而面临健康风险而更加依赖这些工具。与此同时，世界各地正在就如何在保护言论自由和隐私的同时改善内容审核进行重要的政策辩论。为了推进这些辩论，我们需要了解自动化内容分析工具的潜在作用。本文解释了分析在线多媒体内容的工具的能力和局限性，并强调了大规模使用这些工具而不考虑其局限性的潜在风险。它集中在两个主要类别的工具：匹配模型和计算机预测模型。匹配模型包括加密和感知哈希，将用户生成的内容与现有和已知的内容进行比较。预测模型（包括计算机视觉和计算机听觉）是机器学习技术，旨在识别新的或以前未知的内容的特征。
英文摘要：
The ever-increasing amount of user-generated content online has led, in recent years, to an expansion in research and investment in automated content analysis tools. Scrutiny of automated content analysis has accelerated during the COVID-19 pandemic, as social networking services have placed a greater reliance on these tools due to concerns about health risks to their moderation staff from in-person work. At the same time, there are important policy debates around the world about how to improve content moderation while protecting free expression and privacy. In order to advance these debates, we need to understand the potential role of automated content analysis tools. This paper explains the capabilities and limitations of tools for analyzing online multimedia content and highlights the potential risks of using these tools at scale without accounting for their limitations. It focuses on two main categories of tools: matching models and computer prediction models. Matching models include cryptographic and perceptual hashing, which compare user-generated content with existing and known content. Predictive models (including computer vision and computer audition) are machine learning techniques that aim to identify characteristics of new or previously unknown content.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
