Machine Learning Classification of AI and Real Images

The rapid advancement of artificial intelligence (AI) led to the proliferation of AI-generated images, raising significant concerns about the authenticity of visual content in various domains, including social media, journalism, and creative industries. This study explored the application of machine learning techniques to classify AI-generated and real images, employing a systematic approach that combined traditional machine learning models with advanced neural network architectures. The research developed support vector machines (SVM) with feature extraction methods such as histograms of oriented gradients (HOG) and convolutional neural network (CNN)-based features. Additionally, more sophisticated models, including ensemble neural networks, were implemented to improve classification performance. A voting mechanism was introduced to aggregate predictions across multiple models, leveraging their complementary strengths to enhance robustness and accuracy. The methodology addressed the technical challenges of distinguishing AI-generated images and provided a scalable framework for practical implementation in real-world applications. This study underscored the critical role of machine learning in maintaining digital authenticity and set the stage for future advancements in automated image classification.