Digital Image Forgery Detection Using Transfer Learning

ArXi:2605.08167v1 Announce Type: cross The increasing availability of advanced image editing tools has led to a significant rise in manipulated digital content, posing serious challenges for digital forensics and information security. This study presents a transfer learning-based framework for digital image forgery detection that integrates compression-aware feature enhancement with deep convolutional neural network (CNN) architectures.