• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.15-20
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• 2019

Fundamental processes in digital forensic investigation - such as disk imaging - were developed when digital investigation was relatively young. As digital forensic processes and procedures matured, these fundamental tools, that are the pillars of the reset of the data processing and analysis phases of an investigation, largely stayed the same. This work is a study of modern digital forensic imaging software tools. Specifically, we will examine the feature sets of modern digital forensic imaging tools, as well as their development and release cycles to understand patterns of fundamental tool development. Based on this survey, we show the weakness in current digital investigation fundamental software development and maintenance over time. We also provide recommendations on how to improve fundamental tools.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.178-192
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• 2021

File system forensics typically focus on the contents or timestamps of a file, and it is common to work around file/directory centers. But to recover a deleted file on the disk or use a carving technique to find and connect partial missing content, the evidence must be analyzed using cluster-centered analysis. Forensics tools such as EnCase, TSK, and X-ways, provide a basic ability to get information about disk clusters, but these are not the core functions of the tools. Alternatively, Sysinternals' DiskView tool provides a more intuitive visualization function, which makes it easier to obtain information around disk clusters. In addition, most current tools are for Windows. There are very few forensic analysis tools for MacOS, and furthermore, cluster analysis tools are very rare. In this paper, we developed a tool named FACT (Forensic Analyzer based Cluster Information Tool) for analyzing the state of clusters in a HFS+ file system, for digital forensics. The FACT consists of three features, a Cluster based analysis, B-tree based analysis, and Directory based analysis. The Cluster based analysis is the main feature, and was basically developed for cluster analysis. The FACT tool's cluster visualization feature plays a central role. The FACT tool was programmed in two programming languages, C/C++ and Python. The core part for analyzing the HFS+ filesystem was programmed in C/C++ and the visualization part is implemented using the Python Tkinter library. The features in this study will evolve into key forensics tools for use in MacOS, and by providing additional GUI capabilities can be very important for cluster-centric forensics analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3751-3770
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• 2015

Many sorts of image processing software facilitate image editing and also generate a great number of doctored images. Forensic technology emerges to detect the unintentional or malicious image operations. Most of forensic methods focus on the detection of single operations. However, a series of operations may be used to sequentially manipulate an image, which makes the operation detection problem complex. Forensic investigators always want to know as much exhaustive information about a suspicious image's entire processing history as possible. The detection of the operation chain, consisting of a series of operations, is a significant and challenging problem in the research field of forensics. In this paper, based on the histogram distribution uniformity of a manipulated image, we propose an operation chain detection scheme to identify histogram equalization (HE) followed by the dither-like operation (DLO). Two histogram features and a local spatial feature are utilized to further determine which DLO may have been applied. Both theoretical analysis and experimental results verify the effectiveness of our proposed scheme for both global and local scenarios.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.421-428
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• 2021

The High Efficiency Image File Format (HEIF) is an MPEG-developed image format that utilizes the video codec H.265 to store still screens in a single image format. The iPhone has been using HEIF since 2017, and Android devices such as the Galaxy S10 have also supported the format since 2019. The format can provide images with good compression rates, but it has a complex internal structure and lacks significant compatibility between devices and software, making it not popular to replace commonly used JPEG (or JPG) files. However, despite the fact that many devices are already using HEIF, digital forensics research regarding it is lacking. This means that we can be exposed to the risk of missing potential evidence due to insufficient understanding of the information contained inside the file during digital forensics investigations. Therefore, in this paper, we analyze the HEIF formatted photo file taken on the iPhone and the motion photo file taken on the Galaxy to find out the information and features contained inside the file. We also investigate whether or not the software we tested support HEIF and present the requirement of forensic tools to analyze HEIF.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.11-19
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• 2019

Against advanced image-related crimes, a high level of digital forensic methods is required. However, feature-based methods are difficult to respond to new device features by utilizing human-designed features, and deep learning-based methods should improve accuracy. This paper proposes a deep learning model to identify camera models based on DenseNet, the recent technology in the deep learning model field. To extract camera sensor features, a HPF feature extraction filter was applied. For camera model identification, we modified the number of hierarchical iterations and eliminated the Bottleneck layer and compression processing used to reduce computation. The proposed model was analyzed using the Dresden database and achieved an accuracy of 99.65% for 14 camera models. We achieved higher accuracy than previous studies and overcome their disadvantages with low accuracy for the same manufacturer.