  Prior art has shown it is possible to estimate, through image processing and
computer vision techniques, the types and parameters of transformations that
have been applied to the content of individual images to obtain new images.
Given a large corpus of images and a query image, an interesting further step
is to retrieve the set of original images whose content is present in the query
image, as well as the detailed sequences of transformations that yield the
query image given the original images. This is a problem that recently has
received the name of image provenance analysis. In these times of public media
manipulation ( e.g., fake news and meme sharing), obtaining the history of
image transformations is relevant for fact checking and authorship
verification, among many other applications. This article presents an
end-to-end processing pipeline for image provenance analysis, which works at
real-world scale. It employs a cutting-edge image filtering solution that is
custom-tailored for the problem at hand, as well as novel techniques for
obtaining the provenance graph that expresses how the images, as nodes, are
ancestrally connected. A comprehensive set of experiments for each stage of the
pipeline is provided, comparing the proposed solution with state-of-the-art
results, employing previously published datasets. In addition, this work
introduces a new dataset of real-world provenance cases from the social media
site Reddit, along with baseline results.
