Classification of Artificial Intelligence-Generated and Real Images by Deep Learning

With VGG, ResNet, and EfficientNet models being the most potent image classifiers, this research study compares and contrasts these three well-known CNN architectures in an effort to determine which is best for image classification tasks. In recent years, deep learning has made great strides in the field of computer vision; as a result, comparing various CNN models to find the best results has become crucial. Right model choice from VGG, residual connection of ResNet and resolutions of EfficientNet are just few unique This research engages in an extensive assessment procedure that specifically determines the models' accuracy, efficiency, and generalization ability using benchmark datasets, and adopted methods. The study shows the pros and cons of each model and they are adapted for specific purposes depending on the outcome of the trials and analysis, if successful. An additional finding to the conversation(s), relating to deep learning architectures, which also helps researchers and practitioners to select their best model(s) is that of the pictures in the classification tasks.