Edge-Enhanced Vision Transformer Framework for Accurate AI-Generated Image Detection

TL;DR

This paper introduces a hybrid detection framework combining a fine-tuned Vision Transformer with an edge-based image processing module to improve the accuracy and efficiency of AI-generated image detection.

Contribution

It proposes a novel edge-based module integrated with ViT, enhancing structural cue sensitivity for more accurate AI-generated image detection.

Findings

Achieves 97.75% accuracy on CIFAKE dataset

Outperforms state-of-the-art models in detection performance

Demonstrates effectiveness on both images and video frames

Abstract

The rapid advancement of generative models has led to a growing prevalence of highly realistic AI-generated images, posing significant challenges for digital forensics and content authentication. Conventional detection methods mainly rely on deep learning models that extract global features, which often overlook subtle structural inconsistencies and demand substantial computational resources. To address these limitations, we propose a hybrid detection framework that combines a fine-tuned Vision Transformer (ViT) with a novel edge-based image processing module. The edge-based module computes variance from edge-difference maps generated before and after smoothing, exploiting the observation that AI-generated images typically exhibit smoother textures, weaker edges, and reduced noise compared to real images. When applied as a post-processing step on ViT predictions, this module enhances sensitivity to fine-grained structural cues while maintaining computational efficiency. Extensive experiments on the CIFAKE, Artistic, and Custom Curated datasets demonstrate that the proposed framework achieves superior detection performance across all benchmarks, attaining 97.75% accuracy and a 97.77% F1-score on CIFAKE, surpassing widely adopted state-of-the-art models. These results establish the proposed method as a lightweight, interpretable, and effective solution for both still images and video frames, making it highly suitable for real-world applications in automated content verification and digital forensics.