Enhancing Interpretability in AI-Generated Image Detection with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Lin:2023:ICDMW,
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author = "Mingqian Lin and Lin Shang and Xiaoying Gao",
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booktitle = "2023 IEEE International Conference on Data Mining
Workshops (ICDMW)",
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title = "Enhancing Interpretability in {AI-Generated} Image
Detection with Genetic Programming",
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year = "2023",
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pages = "371--378",
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abstract = "IGC can produce realistic AI-generated images that
challenge human perception. Detecting AI-generated
content is critical, which has prompted the technology
to tell apart real images from the generated ones.
However, the existing methods, such as CNND, LGrad,
lack interpretability. Unlike traditional image
classification, it is crucial to know why the image can
be considered as AI-generated. We introduce a novel
AI-generated image detector based on genetic
programming (GP), prioritizing both interpretability
and classification accuracy. This application of GP in
this context emphasizes the need for interpretability
in AI-generated content identification. Our GP-based
approach not only achieves competitive classification
accuracy but also provides transparent decision-making
processes, bridging the interpretability gap. This
method enhances trust and understanding in the
AI-generated image detection process. Through extensive
experiments, we highlight the potential of GP-based
detectors for this unique task. This research
contributes to improving the transparency and
reliability of AI-generated image detection, holding
implications for computer vision and image forensics.
Our work emphasizes the pivotal role of
interpretability in distinguishing AI-generated content
and offers insights into the inner workings of such
models and also achieves a good generation ability.",
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keywords = "genetic algorithms, genetic programming, Image
forensics, Decision making, Detectors, Reliability,
Task analysis, Image classification, AI-generated image
detection, Interpretability, Transparency",
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DOI = "doi:10.1109/ICDMW60847.2023.00053",
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ISSN = "2375-9259",
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month = dec,
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notes = "Also known as \cite{10411549}",
- }
Genetic Programming entries for
Mingqian Lin
Lin Shang
Xiaoying (Sharon) Gao
Citations