Image Domain Distinct Native Attribute Fingerprinting for Image Forgery Classification
Files
Date
2025-01-07
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
7110
Ending Page
Alternative Title
Abstract
Image forgery is becoming more difficult to detect due to advances in AI image generation. As such, the usefulness ā and even requirement ā 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 %CĪ = ā9% 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.
Description
Keywords
Cyber Operations, Defense, and Forensics, classification, fingerprinting, image forgery, perception dominance
Citation
Extent
10
Format
Geographic Location
Time Period
Related To
Proceedings of the 58th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.