Identifying The Authenticity of Images Using Deep Learning Techniques

Abstract

The rapid development of generative artificial intelligence has produced a wave of hyper-realistic deepfakes, posing existential challenges for authenticity verification to occur in digital media. This study proposes a deep learning architecture for the binary classification of AI-generated and real images as a reaction to a growing need for credible detection techniques. We comparatively evaluate four various architectures: ResNetRS50, MobileNetV2, EfficientNetB0, and a specially designed CNN with integrated Gabor filters and attention mechanisms. All models were trained and evaluated on an equalized, high-quality dataset under the same experimental conditions to provide serious benchmarking. While MobileNetV2 and EfficientNetB0 achieved higher peak validation accuracies of 99.29% and 99.81% respectively, ResNetRS50 was the most powerful and most generalized model. Its robust convergence behavior, high interpretability, and resistance to overfitting— particularly under extended training durations and high-density data—make it the top choice even at a slightly lower peak accuracy of 97.24%. Extended testing using classification reports, confusion matrices, and performance curves supports this conclusion further. A web interface was also established to demonstrate real-time deployment capability, showing that the model is usable in practical applications. The proposed method not only elevates the state of AI image forensics but also serves as a basis for large-scale and trustworthy content verification systems in the face of rising synthetic media.