Text-Independent Algorithm for Source Printer Identification Based on燛nsemble Learning

Abady, Naglaa F. El; Taha, Mohamed; Zayed, Hala H.

doi:10.32604/cmc.2022.028044

Cited by 2 publications

(4 citation statements)

References 19 publications

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“…As shown in Fig. 10, our third proposed technique (SPI-CNN) outperforms [37], [14], [12], [12], and [30] on dataset 2 of 20 printers and 1200 documents for both textural and deep-learned features. Fig.…”

Section: Discussion and Comparisonmentioning

confidence: 84%

“…The accuracy was 86.5% using a combination of Naive Bayes, k-NN, Random Forest classifiers, a straightforward majority voting system, and adaptive boosting techniques. A text-independent algorithm for detecting document forgeries based on source printer identification SPI is suggested by [30]. The image is divided into the top, middle, and bottom sections.…”

Section: Related Workmentioning

confidence: 99%

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

Abady¹,

Zayed²,

Taha³

2023

IJACSA

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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.

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Section: Discussion and Comparisonmentioning

confidence: 84%

Section: Related Workmentioning

confidence: 99%

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

Abady¹,

Zayed²,

Taha³

2023

IJACSA

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“…This section compares the results of three models with some recent algorithms, including Elkasrawi et al [37], CNN [14], KPNF+SURF+ORB [12], SURF and ORB with AdaBoost [16] and HOG+LBP with AdaBoost [18]. Comparison with related work on the dataset of 20 printers is highlighted in Figure 8.…”

Section: Comparison With Other Techniquesmentioning

confidence: 99%

“…Using a combination of Naive Bayes, k-NN, Random Forest classifiers, a basic majority voting system, and adaptive boosting algorithms, the accuracy was 86.5%. Based on source printer identification (SPI), a text-independent technique for identifying document forgeries is proposed [18]. The top, middle, and bottom parts of the image are divided.…”

Section: Related Workmentioning

confidence: 99%

Source printer identification using convolutional neural network and transfer learning approach

F. El Abady,

H. Zayed,

Taha

2024

IJ-AI

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<span>In recent years, Source printer identification has become increasingly important for detecting forged documents. A printer's distinguishing feature is its fingerprints. Each printer has a unique collection of fingerprints on every printed page. A model for identifying the source printer and classifying the questioned document into one of the printer classes is provided by source printer identification. A paper proposes a new approach that trains three different approaches on the dataset to choose the more accurate model for determining the printer's source. In the first, some pre-trained models are used as feature extractors, and support vector machine (SVM) is used to classify the generated features. In the second, we construct a two-dimensional convolutional neural network (2D-CNN) to address the source printer identification (SPI) problem. Instead of SoftMax, 2D-CNN is employed for feature extractors and SVM as a classifier. This approach obtains 93.75% 98.5% accuracy for 2D-CNN-SVM in the experiments. The SVM classifier enhanced the 2D-CNN accuracy by roughly 5% over the initial configuration. Finally, we adjusted 13 already-pre-trained CNN architectures using the dataset. Among the 13 pre-trained CNN models, DarkNet-19 has the greatest accuracy of 99.2 %. On the same dataset, the suggested approaches achieve well in terms of classification accuracy than the other recently released algorithms. </span>

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Text-Independent Algorithm for Source Printer Identification Based on燛nsemble Learning

Cited by 2 publications

References 19 publications

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

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

Source printer identification using convolutional neural network and transfer learning approach

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