Laser Printer Identification Using Convolutional Neural Network for Forensic Document Authentication

Document forgery has become easier with the advancement of printing technologies and image editing software. Identifying the source printer of a printed document is an important task in forensic document analysis. Traditional approaches rely on handcrafted texture features such as Local Binary Pattern (LBP), Local Directional Pattern (LDP), and Local Optimal Oriented Pattern (LOOP). However, these methods require manual feature extraction and often fail to capture complex intrinsic printer signatures effectively. This research proposes a deep learning-based approach using Convolutional Neural Networks (CNN) to automatically identify laser printer models based on texture patterns observed in printed documents. The CNN model learns discriminative features from character-level images without requiring handcrafted descriptors. The dataset consists of scanned document images printed from ten different laser printers, and character-level segmentation is applied to extract the character ‘e’ images. The proposed CNN-based method achieves high classification accuracy and demonstrates superior performance compared to traditional machine learning approaches such as SVM with handcrafted features. The results show that CNN can effectively capture intrinsic printer signatures and improve document authentication systems in forensic applications.

Laser printer, CNN, RELU, Grayscale.

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