AI Generated Image Detection Using Neural Networks

Abstract

This abstract introduces a breakthrough in image detection using Convolutional Neural Networks (CNNs). Renowned for their ability to recognize local features, share weights, and employ pooling mechanisms, CNNs form the foundation of this innovative approach. The study presents a novel method that harnesses CNNs' inherent strengths to elevate image detection. Central to this method is the incorporation of a specialized module called "ShortCut3- ResNet," inspired by the Residual Network (ResNet) concept. This module enhances the network's capacity to capture intricate image details, thereby facilitating more precise feature extraction. An integral facet of the proposed technique is the establishment of a dual optimization model. By harmonizing the convolution and full connection processes within the CNN, this model amplifies the network's ability to understand intricate image patterns. By systematically exploring a spectrum of CNN parameter configurations, the optimal setting is identified. This approach markedly enhances the CNN's proficiency in extracting pertinent features from images, leading to substantial improvements in its image recognition accuracy. In summary, this abstract underscore a pioneering advancement in image detection, leveraging the prowess of CNNs. By integrating a specialized module and refining the learning process, this technique augments the CNN's capability to discern complex image patterns. This innovation holds transformative potential, spanning applications that range from refining image recognition systems to enhancing the precision of AI-generated image detection. Through this advancement, the intersection of CNNs and image detection propels the field towards new horizons of accuracy and efficacy. Keyword: Image detection, Convolutional Neural Networks (CNNs), Local features, Share weights, Pooling mechanisms, ShortCut3-ResNet, Residual Network (ResNet), Feature extraction.