Learning deep features for source color laser printer identification based on cascaded learning

TL;DR

This paper introduces a cascaded deep learning approach for identifying source color laser printers, utilizing adversarial training and knowledge transfer to improve accuracy and robustness against transformations.

Contribution

The paper proposes a novel cascaded deep neural network framework with adversarial refinement and transfer learning for printer identification, considering rotation and scaling robustness.

Findings

Outperforms existing printer identification methods

Effective in handling rotation and scaling variations

Achieves higher accuracy on eight different printers

Abstract

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.