Integrating Multicore SVM With Enhanced Residual Networks for AI Content Recognition

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.