Enhancing Interpretability in AI-Generated Image Detection with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{Lin:2023:ICDMW,

• author = "Mingqian Lin and Lin Shang and Xiaoying Gao",
• booktitle = "2023 IEEE International Conference on Data Mining Workshops (ICDMW)",
• title = "Enhancing Interpretability in {AI-Generated} Image Detection with Genetic Programming",
• year = "2023",
• pages = "371--378",
• 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.",
• keywords = "genetic algorithms, genetic programming, Image forensics, Decision making, Detectors, Reliability, Task analysis, Image classification, AI-generated image detection, Interpretability, Transparency",
• DOI = "doi:10.1109/ICDMW60847.2023.00053",
• ISSN = "2375-9259",
• month = dec,
• notes = "Also known as \cite{10411549}",

}

Genetic Programming entries for Mingqian Lin Lin Shang Xiaoying (Sharon) Gao

Citations