Exploring the Efficacy and Limitations of Histogram-Based Fake Image Detection

Generative image models pose challenges to image authenticity and trustworthiness, blurring the line between real and fake content. This paper addresses these concerns by proposing a histogram-based approach using pre-trained models (vgg16, ResNet50, Xception) to train classification networks for distinguishing real from generated images. Leveraging histograms derived from images, the method aims to accurately classify images as authentic or synthetic. Through experiments, the paper examines the effectiveness of the approach in mitigating the risks associated with fake contents widespread dissemination. Results demonstrate promising advancements in detecting image manipulation and preserving the integrity of visual information amidst the spread of generative models. Using pre-trained models the paper shows high classification accuracy for detecting fake images.