This paper introduces a new benchmark for large-scale image similarity
detection. This benchmark is used for the Image Similarity Challenge at
NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a
modified copy of any image in a reference corpus of size 1~million. The
benchmark features a variety of image transformations such as automated
transformations, hand-crafted image edits and machine-learning based
manipulations. This mimics real-life cases appearing in social media, for
example for integrity-related problems dealing with misinformation and
objectionable content. The strength of the image manipulations, and therefore
the difficulty of the benchmark, is calibrated according to the performance of
a set of baseline approaches. Both the query and reference set contain a
majority of "distractor" images that do not match, which corresponds to a
real-life needle-in-haystack setting, and the evaluation metric reflects that.
We expect the DISC21 benchmark to promote image copy detection as an important
and challenging computer vision task and refresh the state of the art. Code and
data are available at this https URL

本文介绍了一个大规模图像相似性检测的新基准（ISC2021），目的是检测查询图像是否是参考样本集（大小为 1 百万） 中任何图像的修改副本，并提供了各种图像变换以模拟出现在社交媒体中的实际问题，以便解决有关虚假信息和不良内容的完整性问题，并根据一组基线方法的性能来校准其难度，预计该基准将促进图像拷贝检测。