  As image tampering becomes ever more sophisticated and commonplace, the need
for image forensics algorithms that can accurately and quickly detect forgeries
grows. In this paper, we revisit the ideas of image querying and retrieval to
provide clues to better localize forgeries. We propose a method to perform
large-scale image forensics on the order of one million images using the help
of an image search algorithm and database to gather contextual clues as to
where tampering may have taken place. In this vein, we introduce five new
strongly invariant image comparison methods and test their effectiveness under
heavy noise, rotation, and color space changes. Lastly, we show the
effectiveness of these methods compared to passive image forensics using Nimble
[https://www.nist.gov/itl/iad/mig/nimble-challenge], a new, state-of-the-art
dataset from the National Institute of Standards and Technology (NIST).
