Digital Library[ Search Result ]
Effective Detection of Generated Images Using Frequency Transform
Hyoungwon Seo, Dongsu Kim, Seoyoen Oh, Jisang Lee, Haneol Jang
http://doi.org/10.5626/JOK.2025.52.4.350
In today's digital era, advanced image generation techniques have produced counterfeit images that are nearly indistinguishable from real ones, thereby undermining the trustworthiness of digital information. Conventional machine learning and deep learning methods have shown limitations when confronting these evolving generative algorithms. This study introduces a novel approach that can analyze characteristics of generated images in the frequency domain. Specifically, we independently applied the Fast Fourier Transform (FFT) and the Discrete Cosine Transform (DCT) to evaluate the effectiveness of each method for detecting generated images. Experimental results revealed that the FFT-based model improved the test accuracy by approximately 12.8%, while the DCT-based model demonstrated a performance enhancement of about 22.2%. These findings confirm that a frequency domain approach outperforms traditional spatial domain-based detection techniques. It is expected to make a substantial contribution to enhancing image reliability in digital forensics.
Type-Checking-based Refinement for the Analysis of Uncaught Exceptions in Digital Forensic Software
Seowoo Lee, Dongwon Lee, Sehoon Kim
http://doi.org/10.5626/JOK.2023.50.12.1071
This paper designs an uncaught exception detection scheme for digital forensic software written in Python, aiming at enhancing the reliability of the forensic process. Inherited from the legacy set-constraint-based analysis method, the proposed scheme identifies potential uncaught exceptions in the target forensic software. Next, with the help of Pyright, a Python-specific static type checker, it is possible to eliminate meaningless alarms inevitably created during the analysis process, such as key errors in list types or out-of-range index errors in dictionary types. In addition, we remove duplicated detections based on the dependency tree which traces the inclusion relationship between each component or point of a given module. The experiment results, obtained by applying our static analyzer to nine benchmarks of digital forensic software, demonstrate that the proposed scheme successfully finds 10 locations of three exception patterns, including dictionary key errors, out-of-range index errors, and division by zero errors, which could not be located before. Furthermore, the analysis achieves an average of 84% and a maximum of 89% reduction in false alarms for each benchmark.
Gender Classification Model Based on Colloquial Text in Korean for Author Profiling of Messenger Data
Jihye Kang, Minho Kim, Hyuk-Chul Kwon
http://doi.org/10.5626/JOK.2023.50.12.1063
With explosive social network services (SNS) growth, there has been an extensive generation of text data through messenger services. In addition, various applications such as Sentiment Analysis, Abusive text Detection, and Chatbot have been developed and provided due to the recent development of Natural Language Processing. However, there has not been an attempt to classify various characteristics of authors such as the gender and age of speakers in Korean colloquial texts. In this study, I propose a gender classification model for author profiling using Korean colloquial texts. Based on Kakao Talk data for the gender classification of the speaker, the Domain Adaptation is carried out by additionally learning ‘Nate Pan’ data to KcBERT(Korean Comments BERT) which is learned by Korean comments. Results of experimenting with a model that combines External Lexical Information showed that the performance was improved by achieving an accuracy of approximately 95%. In this study, the self-collected ‘Nate Pan’ data and the "daily conversation" data provided by the National Institute of the Korean Language were used for domain adaptation, and the ‘Korean SNS’ data of AI HUB was used for model learning and evaluation.
A Study on the Architecture of Cyber Public Information Forensic Tools for Investigation to Obtain the Court Evidence Ability
Jeongho Lee, Minchang Kang, HyunSeok Kang, Jaehoon Jang, Homook Cho
http://doi.org/10.5626/JOK.2022.49.6.494
Although recent development in Internet technology has brought many benefits to our lives, numerous dysfunctions, such as Internet-based cybercrime, have also increased. In order to effectively investigate such Internet-based cybercrime, it is essential to collect, store, and process cyber public information from a digital forensics perspective. However, related laws, such as the current Criminal Procedure Act, have not yet explicitly stipulated cyber public information forensics, or deletion of the original data, may also be one of the reasons for this occurrence. In this paper, we propose a novel architecture in processing and disclosing cyber information forensics tool for investigation to secure the legal evidence capability of cyber disclosure information collected between effective investigations and investigations of cybercrime. We also present a technical approach from a digital forensics perspective to demonstrate the integrity, identity, reproducibility, and authenticity of digital evidence to be observed while collecting and storing cyber disclosure information using the proposed tool.
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.
ENF based Detection of Forgery and Falsification of Digital Files due to Quadratic Interpolation
http://doi.org/10.5626/JOK.2018.45.3.311
Recently, the use of digital audio and video as proof in criminal and all kinds of litigation is increasing, and scientific investigation using digital forensic technique is developing. With the development of computing and file editing technologies, anyone can simply manipulate video files, and the number of cases of manipulating digital data is increasing. As a result, the integrity of the evidence and the reliability of the evidence Is required. In this paper, we propose a technique for extracting the Electrical Network Frequency (ENF) through a grid of power grids according to the geographical environment for power supply, and then performing signal processing for peak detection using QIFFT. Through the detection algorithm using the standard deviation, it was confirmed that the video file was falsified with 73% accuracy and the forgery point was found.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr