  Color laser printers have fast printing speed and high resolution, and
forgeries using color laser printers can cause significant harm to society. A
source printer identification technique can be employed as a countermeasure to
those forgeries. This paper presents a color laser printer identification
method based on cascaded learning of deep neural networks. The refiner network
is trained by adversarial training to refine the synthetic dataset for halftone
color decomposition. The halftone color decomposing ConvNet is trained with the
refined dataset, and the trained knowledge is transferred to the printer
identifying ConvNet to enhance the accuracy. The robustness about rotation and
scaling is considered in training process, which is not considered in existing
methods. Experiments are performed on eight color laser printers, and the
performance is compared with several existing methods. The experimental results
clearly show that the proposed method outperforms existing source color laser
printer identification methods.
