Learning deep features for source color laser printer identification based on cascaded learning
This addresses the societal harm of forgeries by providing a more accurate identification technique for color laser printers, though it appears incremental as it builds on existing deep learning approaches.
The paper tackled the problem of identifying the source color laser printer of a document to combat forgeries, achieving superior performance compared to existing methods on a dataset of eight printers.
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.