Progress in Image Source Identification and Forensic Technology

Abstract

Image source identification forensics, as a core branch of passive digital image forensics, aims to identify the source device or generation method through the implicit physical or algorithmic characteristics of the image itself, in order to deal with the authenticity threat posed by image forgery technology. This paper systematically sorts out the technical evolution path of image source identification forensics, compares and analyzes the technical characteristics and limitations of traditional model-based methods and deep learning-driven methods, and discusses the four major sub-directions of camera source identification, computer graphics-generated image forensics, AI-synthesized image forensics, and re-acquired image forensics. Based on the shortcomings of existing research, this paper proposes key research directions such as the need to integrate physical prior knowledge with deep learning models, build a cross-modal general framework, and enhance adversarial robustness and unknown source detection capabilities. It also predicts that multimodal feature fusion, self-supervised learning, and explainable modeling will become the focus of future technological breakthroughs.