Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
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 %CΔ = −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.
Recommended Citation
Mcquagge, Jessica; Rondeau, Christopher; and Temple, Michael, "Image Domain Distinct Native Attribute Fingerprinting for Image Forgery Classification" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 4.
https://aisel.aisnet.org/hicss-58/st/digital_forensics/4
Image Domain Distinct Native Attribute Fingerprinting for Image Forgery Classification
Hilton Waikoloa Village, Hawaii
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 %CΔ = −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.
https://aisel.aisnet.org/hicss-58/st/digital_forensics/4