  In the realm of digital media, the advent of AI-generated synthetic images
has introduced significant challenges in distinguishing between real and
fabricated visual content. These images, often indistinguishable from authentic
ones, pose a threat to the credibility of digital media, with potential
implications for disinformation and fraud. Our research addresses this
challenge by employing machine learning techniques to discern between
AI-generated and genuine images. Central to our approach is the CIFAKE dataset,
a comprehensive collection of images labeled as "Real" and "Fake". We refine
and adapt advanced deep learning architectures like ResNet, VGGNet, and
DenseNet, utilizing transfer learning to enhance their precision in identifying
synthetic images. We also compare these with a baseline model comprising a
vanilla Support Vector Machine (SVM) and a custom Convolutional Neural Network
(CNN). The experimental results were significant, demonstrating that our
optimized deep learning models outperform traditional methods, with DenseNet
achieving an accuracy of 97.74%. Our application study contributes by applying
and optimizing these advanced models for synthetic image detection, conducting
a comparative analysis using various metrics, and demonstrating their superior
capability in identifying AI-generated images over traditional machine learning
techniques. This research not only advances the field of digital media
integrity but also sets a foundation for future explorations into the ethical
and technical dimensions of AI-generated content in digital media.
