A study on the use of perceptual hashing to detect manipulation of embedded messages in images

TL;DR

This paper investigates how perceptual hashing can be used to detect malicious manipulation of embedded messages in images, comparing different embedding and compression methods to improve robustness.

Contribution

It introduces a method to distinguish between image compression and malicious manipulation using perceptual hashes, highlighting effective embedding and compression techniques.

Findings

Integer wavelet transform embedding performs well

Karhunen-Loeve-transform compression yields good results

Complete distinction between manipulation and compression remains challenging

Abstract

Typically, metadata of images are stored in a specific data segment of the image file. However, to securely detect changes, data can also be embedded within images. This follows the goal to invisibly and robustly embed as much information as possible to, ideally, even survive compression. This work searches for embedding principles which allow to distinguish between unintended changes by lossy image compression and malicious manipulation of the embedded message based on the change of its perceptual or robust hash. Different embedding and compression algorithms are compared. The study shows that embedding a message via integer wavelet transform and compression with Karhunen-Loeve-transform yields the best results. However, it was not possible to distinguish between manipulation and compression in all cases.