Vision Transformer Based Digital Image Forgery Detection and Localization Using Global Contextual Feature Learning

Abstract

Artificial intelligence has significantly improved digital image editing capabilities, making it increasingly difficult to distinguish authentic images from manipulated ones [5, 7]. This paper proposes a Vision Transformer (ViT)-based framework for digital image forgery detection and localization by leveraging global contextual feature learning [4]. Unlike conventional Convolu-tional Neural Networks (CNNs), Vision Transformers capture long-range dependencies through self-attention mechanisms, enabling more effective identification of manipulated regions [4, 9]. The proposed framework performs image preprocessing, patch extraction, positional encod-ing, transformer-based feature learning, binary classification, and forgery localization. The model is evaluated using publicly available benchmark datasets, including CASIA V2, Co-MoFoD, and FaceForensics++ [20, 48], and its performance is assessed using Accuracy, Pre-cision, Recall, F1-score, Area Under Curve (AUC), Intersection over Union (IoU), and Pixel Accuracy [17, 49]. Experimental results demonstrate that the proposed Vision Transformer framework outperforms conventional CNN-based methods in terms of detection accuracy and localization precision [16, 19]. The proposed approach provides a robust and scalable solution for modern digital image forensics [15] and can be extended to hybrid transformer architectures and video forgery detection in future work.