# Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization

## TL;DR

This paper introduces a large-scale image forensics approach that leverages context retrieval and five new invariant comparison methods to improve forgery detection and localization, tested on a NIST dataset.

## Contribution

It presents a novel large-scale forensic method using image retrieval and five invariant comparison techniques for improved tampering localization.

## Key findings

- Effective in noisy, rotated, and color-changed conditions
- Outperforms passive forensics on NIST Nimble dataset
- Enables large-scale forgery analysis of one million images

## Abstract

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).