Digital Image Forgery Detection using Hierarchical Learning

Digital image forgery has emerged as a critical challenge in the digital era, with deep learning techniques offering promising solutions for detecting manipulated images. This paper presents a comprehensive approach to image forgery detection using convolutional neural networks (CNNs) and deep learning models such as ResNet, EfficientNet, and Vision Transformers (ViTs). The methodology involves dataset preprocessing, feature extraction, and classification of forged versus authentic images. Performance evaluation metrics such as accuracy, precision, recall, and F1-score are used to compare different models. The proposed framework enhances the reliability of digital image authentication, ensuring robust forensic analysis for cybersecurity, media integrity, and legal evidence verification. Experimental results demonstrate the efficacy of the deep learning-based approach in detecting various forgery types, including copy-move, splicing, and GAN-generated fake images.