Forensic Discrimination between Traditional and Compressive Imaging Systems

TL;DR

This paper develops a forensic method to distinguish between traditional and compressive imaging systems by modeling their imaging pipelines, extracting blur kernels, and using machine learning classifiers, achieving promising identification accuracy.

Contribution

It introduces a systematic modeling approach for compressive sensing imaging systems and applies machine learning to classify images based on their degradation kernels, advancing forensic analysis techniques.

Findings

Successful discrimination between imaging systems using blur kernels.

Effective classification with CNN and SVM models.

Open-source simulation codes for further research.

Abstract

Compressive sensing is a new technology for modern computational imaging systems. In comparison to widespread conventional image sensing, the compressive imaging paradigm requires specific forensic analysis techniques and tools. In this regards, one of basic scenarios in image forensics is to distinguish traditionally sensed images from sophisticated compressively sensed ones. To do this, we first mathematically and systematically model the imaging system based on compressive sensing technology. Afterwards, a simplified version of the whole model is presented, which is appropriate for forensic investigation applications. We estimate the nonlinear system of compressive sensing with a linear model. Then, we model the imaging pipeline as an inverse problem and demonstrate that different imagers have discriminative degradation kernels. Hence, blur kernels of various imaging systems have utilized as footprints for discriminating image acquisition sources. In order to accomplish the identification cycle, we have utilized the state-of-the-art Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches to learn a classification system from estimated blur kernels. Numerical experiments show promising identification results. Simulation codes are available for research and development purposes.