2020 Nov 12th: Code is now available at
2020 June 3rd: This work has been selected to be published in the August 2020 IEEE
JSTSP Special Issue on Data Driven Media Authentication and Forensics!
Project code will be released to coincide with publication.
We introduce a novel
abstract, graph-based representation of an image, which we
call the Forensic Similarity Graph, that captures key forensic
relationships among regions in the image. In this representation,
small image patches are represented by graph vertices with edges
assigned according to the forensic similarity between patches.
Localized tampering introduces unique structure into this graph,
which aligns with a concept called “community structure” in
graph-theory literature. In the Forensic Similarity Graph, communities correspond to the tampered and unaltered regions in the
image. As a result, forgery detection is performed by identifying
whether multiple communities exist, and forgery localization is
performed by partitioning these communities.
In this work, we also present
community detection techniques to
detect and localize image forgeries utilizing the proposed graph representation. We experimentally show that
the community detection methods, when applied to the Forensic Similarity Graph, outperform existing
state-of-the-art forgery detection and localization methods, which
do not capture such community structure.
Forgery Localization Examples
Here are some examples of using our proposed algorithm for forgery localization, compared against other methods from literature.
This article builds off of several works from the MISL group, including: