Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images

With the application of generation models, internet is increasingly inundated with AI-generated content (AIGC), causing both real and AI-generated content indexed in corpus for search. This paper explores the impact of AI-generated images on text-image search in this scenario. Firstly, we construct a benchmark consisting of both real and AI-generated images for this study. In this benchmark, AI-generated images possess visual semantics sufficiently similar to real images. Experiments on this benchmark reveal that text-image retrieval models tend to rank the AI-generated images higher than the real images, even though the AI-generated images do not exhibit more visually relevant semantics to the queries than real images. We call this bias as invisible relevance bias. This bias is detected across retrieval models with different training data and architectures. Further exploration reveals that mixing AI-generated images into the training data of retrieval models exacerbates the invisible relevance bias. These problems cause a vicious cycle in which AI-generated images have a higher chance of exposing from massive data, which makes them more likely to be mixed into the training of retrieval models and such training makes the invisible relevance bias more and more serious. To mitigate this bias and elucidate the potential causes of the bias, firstly, we propose an effective method to alleviate this bias. Subsequently, we apply our proposed debiasing method to retroactively identify the causes of this bias, revealing that the AI-generated images induce the image encoder to embed additional information into their representation. This information makes the retriever estimate a higher relevance score. We conduct experiments to support this assertion.