Integrating Multicore SVM With Enhanced Residual Networks for AI Content Recognition
Haiqing Zhang · 2024
This study delves into the intersection of data science and machine learning methodologies, specifically examining the recognition of Artificial Intelligence (AI)-generated papers and images. We curate a comprehensive dataset comprising 200 papers and 855 images, meticulously segregating them into AI-generated and non-AI categories, and transform this raw data into structured formats suitable for rigorous analysis. For text classification, we employ a Multicore Support Vector Machines (SVM) model, optimized through cross-validation and grid search techniques, to accurately distinguish AI-authored papers from human-penned ones. For image classification, we develop an enhanced model that builds upon the strengths of ResNet and DenseNet architectures, achieving high accuracy in discerning AI-generated images. Furthermore, we integrate these two classification systems within a weighted, top-level decision framework, offering a holistic approach to AI content recognition. The proposed methodology and findings offer a novel perspective on AI content detection, with potential applications in copyright protection, academic integrity assessment, and media content monitoring. This research contributes to advancing the state-of-the-art in AI content identification and underscores the importance of robust tools for managing the proliferation of AI-generated content.