As visual misinformation becomes increasingly prevalent, platform algorithms act as intermediaries that curate information for users' verification practices. Yet, it remains unclear how algorithmic gatekeeping tools, such as reverse image search (RIS), shape users' information exposure during fact-checking. This study systematically audits Google RIS by reversely searching newly identified misleading images over a 15-day window and analyzing 34,486 collected top-ranked search results. We find that Google RIS returns a substantial volume of irrelevant information and repeated misinformation, whereas debunking content constitutes less than 30% of search results. Debunking content faces visibility challenges in rankings amid repeated misinformation and irrelevant information. Our findings also indicate an inverted U-shaped curve of RIS results page quality over time, likely due to search engine "data voids" when visual falsehoods first appear. These findings contribute to scholarship of visual misinformation verification, and extend algorithmic gatekeeping research to the visual domain.

翻译：随着视觉虚假信息日益普遍，平台算法作为中介，为用户验证实践筛选信息。然而，算法把关工具（如反向图像搜索）如何在事实核查过程中塑造用户信息接触，仍不明确。本研究通过为期15天窗口期内对新发现的误导性图像进行反向搜索，并分析收集到的34,486条顶级搜索结果，系统审计了谷歌反向图像搜索。研究发现，谷歌反向图像搜索返回大量无关信息和重复虚假信息，而辟谣内容仅占搜索结果不足30%。在重复虚假信息和无关信息的包围中，辟谣内容面临排名可见性挑战。研究结果还表明，反向图像搜索结果页质量随时间呈倒U型曲线，这可能源于视觉虚假信息首次出现时搜索引擎的"数据空洞"。这些发现为视觉虚假信息验证研究作出贡献，并将算法把关研究扩展至视觉领域。