Vision Transformers (ViTs) for Feature Extraction and Classification of AI-Generated Visual Designs

Deep learning has become a cornerstone of modern Artificial Intelligence (AI), enabling machines to process and interpret complex visual information with unprecedented accuracy. As AI-generated content becomes more realistic, the ability to distinguish between machine-created and human-created images is increasingly important. This challenge extends beyond technical concerns, influencing digital media credibility, intellectual property rights, and the integrity of visual communication. Developing robust classification models to accurately attribute image origins is crucial for ensuring transparency, preventing misinformation, and upholding artistic authenticity in an era of rapidly evolving generative AI technologies. This study addresses the critical need to differentiate between AI-generated and human-generated aesthetic images through the application of advanced deep learning models. We investigate the effectiveness of advanced deep learning architectures including High Resolution Networks (HRNet), and Vision Transformers (ViT) which are generally accurate when used to infer the creative characteristics of human visual art. The proposed model ViT, employing a mechanism of self-attention to process images as sequences of patches for feature extraction, is examined for its potential to capture global contextual relationships within images, which is essential for recognizing the nuanced differences between AI and human artistry. ViT achieves 97% accuracy shows that superior performance validates its ability, using its transformer structure, to analyze and learn about the complex features of images which disclose their origin as compared to HRNet model of 95%. This research highlights the potential of using sophisticated deep learning techniques to address the challenges of content authenticity in digital media. By leveraging the unique strengths of each model, we provide insights into their applicability and effectiveness in distinguishing between different forms of digital creation, marking a significant step forward in the field of digital forensics and content verification.