We address the task of detecting foiled image captions, i.e. identifying
whether a caption contains a word that has been deliberately replaced by a
semantically similar word, thus rendering it inaccurate with respect to the
image being described. Solving this problem should in principle require a
fine-grained understanding of images to detect linguistically valid
perturbations in captions. In such contexts, encoding sufficiently descriptive
image information becomes a key challenge. In this paper, we demonstrate that
it is possible to solve this task using simple, interpretable yet powerful
representations based on explicit object information. Our models achieve
state-of-the-art performance on a standard dataset, with scores exceeding those
achieved by humans on the task. We also measure the upper-bound performance of
our models using gold standard annotations. Our analysis reveals that the
simpler model performs well even without image information, suggesting that the
dataset contains strong linguistic bias.

本文利用基于显式目标信息的简单的可解释且强大的表示方法解决了探测篡改图片标题的任务，并在标准数据集上取得了优于人类的成绩；同时分析揭示了简单模型即使没有图像信息也能取得良好的表现，表明数据集具有强烈的语言倾向。