Matthew Thomson
Exploring Dataset Diversity for GenAI Image Tampering Localisation in Digital Forensics
Thomson, Matthew; McKeown, Sean; Macfarlane, Rich; Leimich, Petra
Authors
Dr Sean McKeown S.McKeown@napier.ac.uk
Lecturer
Rich Macfarlane R.Macfarlane@napier.ac.uk
Associate Professor
Dr Petra Leimich P.Leimich@napier.ac.uk
Lecturer
Abstract
Generative Artificial Intelligence (GenAI) has significantly increased the sophistication and ease of image tampering techniques, posing challenges for digital forensics in identifying manipulated images. A lack of dataset standardisation hinders the ability to effectively benchmark and compare GenAI detection and localisation techniques, reducing their reliability in digital forensic applications. This paper aims to address this gap by exploring the need for standardised criteria for datasets in digital forensics for benchmarking detection techniques through preliminary experiments.
To address the limited diversity in existing datasets, a small-scale dataset consisting of 240 tampered images, 20 masks and 20 authentic images was developed. This dataset includes four subject image classes (animals, objects, persons, scenery) and three inpainting tools (GLIDE, GalaxyAI, Photoshop). The dataset was tested against 13 localisation algorithms from the Image Forensics MATLAB Toolbox to determine the influencing components that should be considered in the standardisation of testing environments.
Among classes, the animals and persons categories achieved the highest F1-Scores and had a consistently higher performance over the other classes. Of the tools, GLIDE-generated images were consistently shown to be the most challenging to detect. These results lay the groundwork for identifying a set of criteria to develop robust testing environments, enabling the development of more accurate and reliable GenAI tampering detection and localisation techniques.
Citation
Thomson, M., McKeown, S., Macfarlane, R., & Leimich, P. (2025, April). Exploring Dataset Diversity for GenAI Image Tampering Localisation in Digital Forensics. Presented at The Digital Forensics Research Conference Europe (DFRWS EU 2025) Digital Forensics Doctoral Symposium (DFDS), Brno, Czech Republic
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | The Digital Forensics Research Conference Europe (DFRWS EU 2025) Digital Forensics Doctoral Symposium (DFDS) |
Start Date | Apr 1, 2025 |
End Date | Apr 4, 2025 |
Acceptance Date | Dec 14, 2024 |
Deposit Date | Jan 10, 2025 |
Publisher | Association for Computing Machinery (ACM) |
Peer Reviewed | Peer Reviewed |
Keywords | Digital Forensics, Artificial Intelligence (AI), Generative AI (GenAI), AI Manipulation, Inpainting, Image Forgery Localisation |
This file is under embargo due to copyright reasons.
Contact repository@napier.ac.uk to request a copy for personal use.
You might also like
Fingerprinting JPEGs With Optimised Huffman Tables
(2018)
Journal Article
A forensic analysis of streaming platforms on Android OS
(2022)
Journal Article
InfoScout: An interactive, entity centric, person search tool.
(2016)
Presentation / Conference Contribution
Fast Filtering of Known PNG Files Using Early File Features
(2017)
Presentation / Conference Contribution
Microtargeting or Microphishing? Phishing Unveiled
(2020)
Presentation / Conference Contribution
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search