Enhanced Fake Image Detection in Social Media Using Vision Transformer
P. Abirami · International Journal for Research in Applied Science and Engineering Technology · 2025
The rapid expansion of social media has escalated the dissemination of manipulated and fake images, threatening the integrity of digital content. Conventional detection methods often falter when confronted with advanced manipulation techniques. This research presents SahAI, an innovative fake image detection model leveraging a pre trained Vision Transformer (ViT) for effective binary classification of images as real or fake. By adapting the ViT architecture with a custom classifier, SahAI achieves high detection accuracy with minimal retraining. The model identifies tampered images and provides a confidence-based classification output. SahAI demonstrates exceptional performance, attaining a training accuracy of 99.12% and a test accuracy of 97.53%, positioning it as a robust tool for verifying social media content authenticity.