An Efficient Source Printer Identification Model using Convolution Neural Network (SPI-CNN)

Abstract

Document forgery detection is becoming increasingly important in the current era, as forgery techniques are available to even inexperienced users. Source printer identification is a method for identifying the source printer and classifying the questioned document into one of the printer classes. According to what we know, most earlier studies segmented documents into characters, words, and patches or cropped them to obtain large datasets. In contrast, in this paper, we worked with the document as a whole and a small dataset. This paper uses three techniques dependent on CNN to find the document source printer without segmenting the document into characters, words, or patches and with small datasets. Three separate datasets of 1185, 1200, and 2385 documents are used to estimate the performance of the suggested techniques. In the first technique, 13 pre-trained CNN were tested, and they were only used for feature extraction, while SVM was used for classification. In the second technique, a pre-trained neural network is retrained using transfer learning for feature extraction and classification. In the third technique, CNN is trained from scratch and then used for feature extraction and SVM for classification. Many experiments are done in the three techniques, showing that the third technique gives the best result. This technique achieved 99.16%, 99.58%, and 98.3% accuracy for datasets 1, 2, and 3. The three techniques are compared with some previously published papers, and found that the third technique gives better results.