Matching images by similarity consists in identifying the source of an altered image in a large collection of unrelated images. This technology is applied to a range of content moderation domains: misinformation, copyright infringement, scams, etc. In these domains, it has concrete and real-world impact to protect the integrity of persons engaging in social media. This challenge aims at compiling a dataset focused on image similarity in order to provide a benchmark of efforts from academic researchers and industrial actors. The participants will be provided with a reference collection of one million images and a set of query images. The query images are transformed versions of reference images. The transformations include various types of image edition, collages, and re-encoding. The participants are tasked with finding the source image from the dataset. Baseline methods include all techniques from the instance matching literature (keypoint matching, global descriptor extraction). The anticipated scientific impact is to bring back image similarity detection as an important and challenging task in the computer vision domain and refresh the state of the art. Participants could adopt, for example, recent approaches from self-supervised learning.