Image Domain Distinct Native Attribute Fingerprinting for Image Forgery Classification

Authors

Jessica McQuagge, Air Force Institute of Technology
Christopher M. Rondeau, Air Force Institute of TechnologyFollow
Michael A. Temple, Air Force Institute of TechnologyFollow

Document Type

Conference Proceeding

Publication Date

1-7-2025

Abstract

Image forgery is becoming more difficult to detect due to advances in AI image generation. As such, the usefulness — and even requirement — for detection techniques that are affordable (computationally and monetarily) as well as intuitive and simple are equally increasing. This work demonstrates the first adoption of Distinct Native Attribute (DNA) Fingerprinting to image and forgery detection to achieve similar results while mitigating the cost of implementation. General image classification results with accuracy of %C = 98.8% support the overall utility while the ability to detect within-category image forgeries produce an average of %C = 81.8%. Using an intuitive and small set of features, preliminary results show an approximate average classification accuracy difference of only %C_Δ = −9% from more complex solutions. This work demonstrates the ability to adopt DNA Fingerprinting for image classification, and image forgery using Image Domain DNA (ID-DNA) that is holistically less resource intensive while requiring less time, money, and expert knowledge.

Comments

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This is an Open Access conference paper is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. CC BY-NC-ND 4.0

This paper was presented in the HICSS 2025 Conference Minitrack on Cyber Operations, Defense, and Forensics

DOI

HICSS Record: https://hdl.handle.net/10125/109701