MAFN-HFL: Multi-Scale Adaptive Fusion Network For Robust Image Forgery Detection Using Hierarchical Feature Learning

The threat of digital image forgery is increasingly becoming a problem to the authenticity of the media, particularly with the introduction of sophisticated editing software and Generative Artificial Intelligence (GAI). To develop a promising forgery detection framework, this research proposes a Multi-Scale Adaptive Fusion Network with Hierarchical Feature Learning (MAFN-HFL), a new Deep Learning (DL) architecture using multi-scale adaptive feature learning and fusion. The dataset consists of images of various domains, natural scenes, portraits, documents, and medical images, and their forgeries. Preprocessing involves noise removal and performing multi-resolution decomposition. Notably, the proposed MAFNHFL incorporates a Multi-Scale Convolutional Attention Network (MSCAN) that extracts both local and global forgery artifacts, an Adaptive Feature Fusion Module (AFFM) that adaptively fuses multi-resolution features according to the manipulation context, and Hierarchical Feature Learning with skip connections to preserve fine-grained visual details. The features of the spatial and frequency domains are captured by a proposed ResNet-50 and Discrete Cosine Transform (DCT) analysis, respectively, through a dual-branch architecture. An Artifact-Aware Attention mechanism achieves a further focus on tampered regions. The hybrid CNNa-Transformer classifier using a confidence-weighted ensemble is capable of providing binary classification as well as localization of manipulated pixels. The proposed model was evaluated on two benchmark datasets using a 70:30 training-test split. Experimental results show strong performance, achieving classification accuracy as high as 99.21% with consistently high precision and low error rates across both datasets. These findings confirm that the proposed MAFN-HFL is a scalable and efficient solution for next-generation image forgery detection.