Trustworthy Deepfake Detection: Explainable LIME Method of ViT and CNN Architectures

The rapid evolution of generative artificial intelligence has enabled the creation of highly realistic deepfake facial imagery, supporting innovative applications in filmmaking and digital media while simultaneously amplifying risks related to misinformation and public safety. As a result, deepfake detection has become a critical research priority that must combine strong predictive performance with transparent and trustworthy decision-making, since deep learning models remain largely opaque and difficult to interpret in sensitive judicial and information-critical contexts. In this work, we develop and evaluate five deepfake detection architectures, including a Vision Transformer and four Convolutional Neural Networks, trained on the 140K Real and Fake Faces dataset, with the best models achieving an accuracy of 95 percent. To address the fundamental challenge of explainability, we further integrate the LIME interpretability framework, which generates clear and visually intuitive explanations of model decisions, thereby enhancing transparency and strengthening user confidence in automated deepfake analysis.