  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.
