Search : [ author: Junho Jeon ] (2)

A Digital Forensic Process for Ext4 File System in the Flash Memory of IoT Devices

Junho Jeong, Beomseok Kim, Jinsung Cho

http://doi.org/10.5626/JOK.2021.48.8.865

With the recent rapid advances in digital communication technology, the spread of IoT(Internet of Things) has accelerated and IoT devices can be utilized to investigate crimes and accidents due to the close connection between human society and IoT devices. Accordingly, with the increasing importance of digital forensics, numerous studies have been conducted. However, most digital forensics research proposed only abstract methodologies due to the various types of IoT devices. In addition, binwalk, which is actively used as a firmware analysis tool, does not adequately analyze and extract the ext4 file system. To solve these problems, this paper proposes a proper extraction and analysis method and a practical process that could extract the ext4 file system from the flash memory of IoT devices using the binwalk with the proposed method. This study also verifies the proposed process with DJI Phantom 4 Pro V2.0 drone.

Repeated Cropping based on Deep Learning for Photo Re-composition

Eunbin Hong, Junho Jeon, Seungyong Lee

http://doi.org/

This paper proposes a novel aesthetic photo recomposition method using a deep convolutional neural network (DCNN). Previous recomposition approaches define the aesthetic score of photo composition based on the distribution of salient objects, and enhance the photo composition by maximizing the score. These methods suffer from heavy computational overheads, and often fail to enhance the composition because their optimization depends on the performance of existing salient object detection algorithms. Unlike previous approaches, we address the photo recomposition problem by utilizing DCNN, which shows remarkable performance in object detection and recognition. DCNN is used to iteratively predict cropping directions for a given photo, thus generating an aesthetically enhanced photo in terms of composition. Experimental results and user study show that the proposed framework can automatically crop the photo to follow specific composition guidelines, such as the rule of thirds.


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