• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.807-812
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• 2022

Recently, as the number of voice recording files submitted as court evidence increases, the number of cases claiming forgery is also increasing. If the audio recording file structure and metadata, which are objective grounds, are completely forged, it is actually impossible to detect forgery of the sophisticated audio recording file. It is extremely rare for the court to reject the file structure and metadata analysis performed with the forged audio recording file. The purpose of this study is to prove that forgery of voice recording file structure and metadata is easily possible. To this end, in this study, it was introduced that forgery detection is impossible when the 'mixed paste' function, which enables sophisticated editing based on the typification of the editing method of voice recording files, is applied. Moreover, it has been proven through experiments that forgery of file structure and metadata is possible. Therefore, a stricter standard for judging the admissibility of evidence is required when the audio recording file is adopted as digital evidence. This study will not only contribute to the standard of integrity in the adoption of digital evidence by judges, but will also contribute to the method of constructing a dataset for artificial intelligence in detecting forgery of recorded files that is expected to be developed in the future.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1103-1109
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• 2023

Today, as the number of audio files submitted as legal evidence increases with the proliferation of smartphones, the integrity of audio files has become an important issue. Accordingly, the purpose of this study is to explore whether the metadata and file structure of audio files recorded on Samsung smartphones can be manipulated to be identical to the original. This study was based on Samsung smartphones, the most widely used in Korea, and conducted experiments on the built-in voice recording app and the 'Easy Voice Recorder' app, which is the most popular recording app. Through the experiments of this study, it was proven that the metadata and file structure of audio files can be manipulated. Therefore, this study reveals that metadata and file structure analysis have limitations in proving the integrity when audio files are analyzed for adoption as legal evidence. They also argue for the need to develop new voice file forgery technology that does not rely on metadata and file structure analysis.

• Phonetics and Speech Sciences
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• v.6 no.2
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• pp.21-28
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• 2014

We propose a novel scheme for digital audio authentication of given audio files which are edited by inserting small audio segments from different environmental sources. The purpose of this research is to detect inserted sections from given audio files. We expect that the proposed method will assist human investigators by notifying suspected audio section which considered to be recorded or transmitted on different environments. GMM-UBM and GSV-SVM are applied for modeling the dominant environment of a given audio file. Four kinds of likelihood ratio based scores and SVM score are used to measure the likelihood for a dominant environment model. We also use an ensemble score which is a combination of the aforementioned five kinds of scores. In the experimental results, the proposed method shows the lowest average equal error rate when we use the ensemble score. Even when dominant environments were unknown, the proposed method gives a similar accuracy.

• Journal of KIISE
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• v.45 no.3
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• pp.311-320
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• 2018

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.

• Phonetics and Speech Sciences
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• v.14 no.3
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• pp.103-112
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• 2022

Due to the popularization of smartphones, most of the recorded speech files submitted as evidence of recent crimes are produced by smartphones, and the integrity (forgery) of the submitted speech files based on smartphones is emerging as a major issue in the investigation and trial process. Samsung smartphones with the highest domestic market share are distributed with built-in speech recording applications that can record calls and voice, and can edit recorded speech. Unlike editing through third-party speech (audio) applications, editing by their own builtin speech applications has a high similarity to the original file in metadata structures and attributes, so more precise analysis techniques need to prove integrity. In this study, we constructed a speech file metadata database for speech files (original files) recorded by 34 Samsung smartphones and edited speech files edited by their built-in speech recording applications. We analyzed by comparing the metadata structures and attributes of the original files to their edited ones. As a result, we found significant metadata differences between the original speech files and the edited ones.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.73-83
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• 2014

Thanks to the development of diverse audio editing Technology, audio file can be easily revised. As a result, diverse social problems like forgery may be caused. Digital forensic technology is actively studied to solve these problems. In this paper, a hand-held device identification method, an area of digital forensic technology is proposed. It uses the noise features of devices caused by the design and the integrated circuit of each device but cannot be identified by the audience. Wiener filter is used to get the noise sounds of devices and their acoustic features are extracted via MIRtoolbox and then they are trained by multi-layer neural network. To evaluate the proposed method, we use 5-fold cross-validation for the recorded data collected from 6 mobile devices. The experiments show the performance 99.9%. We also perform some experiments to observe the noise features of mobile devices are still useful after the data are uploaded to UCC. The experiments show the performance of 99.8% for UCC data.