Digital forensics of microscopic images for printed source identification

Tsai, Min-Jen; Yuadi, Imam

doi:10.1007/s11042-017-4771-1

Cited by 11 publications

(16 citation statements)

References 43 publications

(70 reference statements)

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“…Furthermore, the digital magnification could be adjusted through the digital imaging processing and shown on the screen. In this study, the microscope OLYMPUS BX41 with the digital camera resolution 3.1 mega pixels and CCD Chip 2048 × 1536 pixels is simulated [28].…”

Section: The Microscope Used In This Studymentioning

confidence: 99%

Deep learning for printed document source identification

Tsai

Tao

Yuadi

2019

Signal Processing: Image Communication

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Section: The Microscope Used In This Studymentioning

confidence: 99%

Deep learning for printed document source identification

Tsai

Tao

Yuadi

2019

Signal Processing: Image Communication

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“…The average experimental results achieve a 98.64% identification rate, which is 1.27% higher than the previously known approach of GLCM. Many important statistical features, such as the Spatial filters, LBP, the Wiener filter, GLCM, the Gabor filter, DWT, Haralick, and SFTA features, are calculated using image processing techniques and data exploration techniques [8]. The highest rate of identification is achieved by the LBP method.…”

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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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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“…Feature selection was applied to reduce the dimensionality of the feature vector while identification was carried out using an SVM. In another subsequent work (Tsai & Yuadi, 2018), textural, spatial and fractal features were investigated to identify printers from microscopic images. High accuracy values of more than 99% were reported on English and Chinese characters.…”

Section: Literature Reviewmentioning

confidence: 99%

Document forgery detection using source printer identification: A comparative study of text‐dependent versus text‐independent analysis

et al. 2022

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Source printer identification represents an interesting modality for document forgery detection. Establishing the identity of the printer that was employed to print a questioned document allows concluding its authenticity. This paper investigates the effectiveness of deep visual features (learned using convolutional neural networks) in characterization of the source printer. Images of printed documents are divided into small patches as well as characters for extraction of features. An off-the-shelf recognition engine is also integrated, allowing experiments in text-dependent as well as text-independent modes. Experiments are carried out on a standard data set of documents from 20 different printers and identification rates of 95.52% and 98.06% are reported using patches and characters, respectively. Furthermore, the discriminating power of different characters, as well as their combinations, is also being studied. Unlike many existing techniques, which rely on pre-segmented characters and report results by comparing same characters only, the proposed technique works on complete images of printed documents and reports high identification rates.

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Digital forensics of microscopic images for printed source identification

Cited by 11 publications

References 43 publications

Deep learning for printed document source identification

Deep learning for printed document source identification

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

Document forgery detection using source printer identification: A comparative study of text‐dependent versus text‐independent analysis

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