DESCRIPTION:
This code implements the algorithms described in "Image Provenance at Scale".
As expressed in the above illustration, it includes 2 main parts:
Each part of this pipeline creates output that is then used as input for the next part of the pipeline.
OVERVIEW:
Image extraction utilizes the Distributed SURF method to extract 5000 local features from each image.
The image index utilizes OPQ, which must be trained on a subset of these features before index construction can begin
The index is built using an Inverted File binned on the trained OPQ centroids using FAISS.
Filtering is a 2 query for each image that returns a JSON list of image ranks from the index
Graph building takes the JSON list and constructs a final provenance graph using the algorithm illustrated above.
REQUIREMENTS:
Python2.7
Python3.6
numpy
opencv
faiss
scipy
scikit-image
matplotlib
psutil
progressbar2
urlparse
joblib
QUESTIONS:
Please contact Joel Brogan ([email protected] ), Aparna Bharati ([email protected]), or Daniel Moreira ([email protected]).