Image Similarity Challenge

Goal of the Competition

Competitors built models to help detect whether a given query image is derived from any of the images in a large reference set.

Content tracing is a crucial component on all social media platforms today, used for such tasks as flagging misinformation and manipulative advertising, preventing uploads of graphic violence, and enforcing copyright protections. But when dealing with the billions of new images generated every day on sites like Facebook, manual content moderation just doesn't scale. They depend on algorithms to help automatically flag or remove bad content.

This competition allowed participants to test their skills in building a key part of that content tracing system, and in so doing contribute to making social media more trustworthy and safe for the people who use it.

A reference image is manipulated to produce new images.
In this challenge competitors built models to detect whether a given query image is derived from a reference set.

There were two tracks to this challenge:

• For the Matching Track, competitors created models that directly detect whether a query image is derived from one of the images in a large corpus of reference images.
• For the Descriptor Track, competitors generated useful vector representations of images (up to 256 dimensions). These descriptors are compared with Euclidean distance to detect whether a query image is derived from one of the images in a large corpus of reference images.

Winning Submissions

See below for links to winning submissions' arXiv papers and code.

Matching Track

Descriptor Track