File Fragment Classification Using Grayscale Image Conversion and Deep Learning in Digital Forensics

Chen, Qian; Liao, Qing; Jiang, Zoe L.; Fang, Junbin; Yiu, Siu-Ming; Xi, Guikai; Li, Rong; Yi, Zhengzhong; Wang, Xuan; Hui, Lucas Chi Kwong; Dong, Liyun; Zhang, En

doi:10.1109/spw.2018.00029

Cited by 29 publications

(27 citation statements)

References 13 publications

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“…Also, depending on the problem area to be identified, the presence or absence of color may be advantageous for discrimination. Even black, white, and gray color may be advantageous, especially in fields such as medical imaging diagnosis [38] or security search [39]. In some cases, preprocessing, or graying, in a color image is performed in order to remove unnecessary features that interfere with interpretation.…”

Section: Image Characteristicsmentioning

confidence: 99%

Impact of chart image characteristics on stock price prediction with a convolutional neural network

Jin

Kwon

2021

PLoS ONE

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Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear models such as AR and MR or nonlinear models such as ANNs using standardized numerical data such as corporate financial data and stock price data. Due to the difficulty of securing a sufficient variety of data, researchers have recently begun using convolutional neural networks (CNNs) with stock price graph images only. However, we know little about which characteristics of stock charts affect the accuracy of predictions and to what extent. The purpose of this study is to analyze the effects of stock chart characteristics on stock price prediction via CNNs. To this end, we define the image characteristics of stock charts and identify significant differences in prediction performance for each characteristic. The results reveal that the accuracy of prediction is improved by utilizing solid lines, color, and a single image without axis marks. Based on these findings, we describe the implications of making predictions only with images, which are unstructured data, without using large amounts of standardized data. Finally, we identify issues for future research.

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Section: Image Characteristicsmentioning

confidence: 99%

Impact of chart image characteristics on stock price prediction with a convolutional neural network

Jin

Kwon

2021

PLoS ONE

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“…These characteristics, however, are insuffi cient to distinguish fi le fragments. Qing Liao, et al (2018) suggest a new method based on fragment to grayscale image conversion and deep learning to retrieve more hidden features and increase classifi cation accuracy [15]. The deep Convolution Neural Network (CNN) model can extract about ten thousand features using multi-layered feature maps and non-linear connections between neurons.…”

Section: File Fragment Classifi Cationmentioning

confidence: 99%

A survey of machine learning applications in digital forensics

Khan¹,

Hanif²,

Muhammad³

2021

Trends Comput Sci Inf Technol

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“…Several researchers also applied deep learning to solve the file fragment classification problem. Chen et al [30] proposed a file fragment classification technique using fragment-to-grayscale image conversion and deep learning. They converted 512-byte fragments into 64 × 64 images and then used the convolutional neural networks (CNN) to create the model.…”

Section: File Fragment Classificationmentioning

confidence: 99%

Hierarchy-Based File Fragment Classification

Bhatt

Mishra

Kabir

et al. 2020

MAKE

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File fragment classification is an essential problem in digital forensics. Although several attempts had been made to solve this challenging problem, a general solution has not been found. In this work, we propose a hierarchical machine-learning-based approach with optimized support vector machines (SVM) as the base classifiers for file fragment classification. This approach consists of more general classifiers at the top level and more specialized fine-grain classifiers at the lower levels of the hierarchy. We also propose a primitive taxonomy for file types that can be used to perform hierarchical classification. We evaluate our model with a dataset of 14 file types, with 1000 fragments measuring 512 bytes from each file type derived from a subset of the publicly available Digital Corpora, the govdocs1 corpus. Our experiment shows comparable results to the present literature, with an average accuracy of 67.78% and an F1-measure of 65% using 10-fold cross-validation. We then improve on the hierarchy and find better results, with an increase in the F1-measure of 1%. Finally, we make our assessment and observations, then conclude the paper by discussing the scope of future research.

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File Fragment Classification Using Grayscale Image Conversion and Deep Learning in Digital Forensics

Cited by 29 publications

References 13 publications

Impact of chart image characteristics on stock price prediction with a convolutional neural network

Impact of chart image characteristics on stock price prediction with a convolutional neural network

A survey of machine learning applications in digital forensics

Hierarchy-Based File Fragment Classification

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