A New Approach to Multimedia Files Carving

Qiu, Weidong; Zhu, Ruiyu; Guo, Jie; Tang, Xiaoming; Liu, Bozhong; Huang, Zheng

doi:10.1109/bibe.2014.31

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…Then, a decision machine model (e.g., naïve Bayes, k ‐nearest neighbors, decision tree, random forest, and support vector machine) is trained using these features. In older research projects, the researchers utilize feature threshold comparison schemes to make decisions about the type of the fragments 11,14 . Usually, these threshold‐based methods do not employ any learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Fragments‐Expert: A graphical user interface MATLAB toolbox for classification of file fragments

Teimouri

Seyedghorban

Amirjani

2020

Concurrency and Computation

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Summary The classification of file fragments of various file formats is an essential task in various applications such as firewalls, intrusion detection systems, antiviruses, web content filtering, and digital forensics. However, the community lacks a suitable software tool that can integrate major methods for feature extraction from file fragments and classification among various file formats. In this article, we present Fragments‐Expert that is a graphical user interface MATLAB toolbox for the classification of file fragments. It provides users with 23 categories of features extracted from file fragments. These features can be employed by seven categories of machine learning algorithms for the task of classification among various file formats.

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Section: Introductionmentioning

confidence: 99%

“…In this case, during the recovery and examination of given evidence, the examiner may deal with a collection of fragments with no available file system information [2,[4][5][6][7][8][9]]. The third practical example is the file recovery process during which the file system structure is damaged or deleted [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Fragments‐Expert: A graphical user interface MATLAB toolbox for classification of file fragments

Teimouri

Seyedghorban

Amirjani

2020

Concurrency and Computation

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show abstract

“…[4][5][6][7][8][9][10][11][12][13][14][15]17 The existing methods are mainly graph based, which consider fragments as vertexes and assign the probabilities that two fragments are adjacent to the costs/weights of the edges. The constructed graph is directed complete graph.…”

Section: Introductionmentioning

confidence: 99%

“…Reassembling fragmented files [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] is to reconstruct digital files from a collection of randomly mixed fragments of these files. It arises in digital forensics and plays a crucial role in seizing digital evidence from scattered digital files.…”

Section: Introductionmentioning

confidence: 99%

Reassembling fragmented BMP files based on padding bytes

Han

Niu

2016

J. Electron. Imaging

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Reassembling fragmented image files plays a crucial role in seizing digital evidence from scattered digital image files. The existing algorithms are mainly graph based, which cast the reassembly problem as a K -vertex disjoint path problem in a directed complete graph, which is an NP-complete problem. Based on the padding bytes in BMP files, we present a method to exclude most impossible paths, which can improve the accuracy and decrease the time complexity of the existing graph-based methods. According to the alignment rule of BMP format, padding bytes must be appended to the end of each row to bring up the length of the row to a multiple of 4 bytes. Hence the fragment, being a vertex of the path which correctly reassembles a file, has a property; its byte values at padding positions must be the padding values. Only the fragments with such property can be candidate fragments for the vertex. On the test dataset which is constructed based on 330 image files, taking eight classical methods as examples, we show that the proposed method produces an accuracy improvement ranging from 32% to 55%, and reduces the run time to a scope from 1/6 to 1/237.

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“…Another technique that is used to recover video is Garfienkel method [10]. This technique can restore video files even if the header or footer is fragmented.…”

Section: Related Workmentioning

confidence: 99%

Corrupted MP4 Carving Using MP4-Karver

Abdi¹,

Mohamad²,

Hasheem³

et al. 2016

ijacsa

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Abstract-In the digital forensic, recovery of deleted and damaged video files play an important role in searching for the evidences. In this paper, MP4-Karver tool is proposed to recover and repair the corrupted videos. Moreover, MP4-Karver extracts frames from video for automatically screen-video to detect illegal cases instead of targeting or watching complete video. Therefore, many existing approaches such as Scalpel's method, Garfienkel, Bi-Fragment Gap Carving, Smart Carving and Frame Based Recovery attempts to recover the videos in different ways, but most of the recovered videos are usually not complete playable. The proposed MP4-Karver focuses on recovery of video files and repair corrupted videos to complete and playable. Experimental results show that the proposed MP4-Karver effectively restores corrupted or damaged video for an improved percentage of the video restoration compared with existing tools.

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A New Approach to Multimedia Files Carving

Cited by 9 publications

References 10 publications

Fragments‐Expert: A graphical user interface MATLAB toolbox for classification of file fragments

Fragments‐Expert: A graphical user interface MATLAB toolbox for classification of file fragments

Reassembling fragmented BMP files based on padding bytes

Corrupted MP4 Carving Using MP4-Karver

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