Detection of AI-Generated Images using Multimodal Approach

Saini, Nancy (2024) Detection of AI-Generated Images using Multimodal Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

Generative Adversarial Networks have come with great challenges in image forensics, making it increasingly hard to distinguish an AI-generated image from an authentic one. In this work, therefore, a multimodal approach using Histogram of Oriented Gradients, Local Binary Patterns, Convolutional Neural Network with Support Vector Machines, and Logistic Regression is proposed for improving classification accuracy. The methodology combines various techniques of feature extraction, which are applied in a unique way to address the deficiencies of single-feature models in detection. On rigorous experimentation, while the SVM model delivered an accuracy of 81.12%, Logistic Regression went a notch higher, with an accuracy of 83.52%, thus outperforming several other existing models. The results were driven by an emphasis on the effectiveness of feature integration in capturing wide arrays of image artifacts for improving accuracy in detection. It points out the requirement of more diverse datasets and sophisticated feature extraction methodologies to further make these detection systems even more robust. Even though this research was focused on images produced by StyleGAN, future work shall be organized with datasets from several GAN architectures in order to increase generalizability and adaptiveness for detection models. Future studies should also aim at increasing the breadth of the dataset used and the adoption of hybrid methodologies so that more adaptability and applicability of the models to the real world would be very possible.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Tomer, Vikas UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	25 Aug 2025 10:51
Last Modified:	25 Aug 2025 10:51
URI:	https://norma.ncirl.ie/id/eprint/8621

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