The purpose of this journal is to share and exchange the results of recent research by Asia-Pacific national researchers, and the purpose of the project is to publish the results of convergence research with the humanities and social sciences, including advanced research in specific fields.
[1] Nam In Park, Kyu-Sun Shim, Oc-Yeub Jeon, A Study on Authentication Analysis Procedure of Digital Audio Files, Journal of Digital Forensics, (2019), Vol.13, No.4, pp.257-270.
DOI: http://doi.org/10.22798/kdfs.2019.13.4.257
[2] https://www.hani.co.kr/arti/society/society_general/1070506.html, Jul 4 (2022)
[3] Jae Wan Park, Won Jun Kwak, John Sanghyun Lee, A Study on Forgery Techniques of Smartphone Voice Recording File Structure and Metadata, Journal of the Convergence on Culture Technology, (2022), Vol.8, No.6, pp.807-812.
DOI: https://doi.org/10.17703/JCCT.2022.8.6.807
[4] Ustubioglu, Arda, Beste Ustubioglu, and Guzin Ulutas, Mel spectrogram-based audio forgery detection using CNN, Signal, Image and Video Processing, (2023), Vol.17, No.5, pp.2211-2219.
DOI: https://doi.org/10.1007/s11760-022-02436-4
[5] MATT REYNOLDS, Courts and lawyers struggle with growing prevalence of deepfakes, ABA Journal, (2020)
Available from: https://www.abajournal.com/web/article/courts-and-lawyers-struggle-with-growing-prevalence-of-deepfakes
[6] Hasan Fayyad-Kazan, Ale Hejase, Imad Moukadem, and Sondos Kassem-Moussa, Verifying the Audio Evidence to Assist Forensic Investigation, Computer and Information Science, (2021), Vol.14, No.3, pp.25-37.
DOI: https://doi.org/10.5539/cis.v14n3p25
[7] Nam In Park, Ji Woo Lee, Seong Ho Lim, Jin-Hwan Kim, Jae Sung Lim, Jung Hwan Lee, Do Joon Jung, Ji-Hun Kim, Jun Seok Byun, Oc-Yeub Jeon, and Gi-Hyun Na, Forgery Analysis Method for Audio Recordings Generated by Voice Recorder Application from Samsung Smartphones, Korean Journal of Forensic Sciences, (2022), Vol.23, No.2 pp.77-80.
DOI: https://doi.org/10.53051/ksfs.2022.23.2.10
[8] Se Jin Park, Ji Won Yoon, ENF based Detection of Forgery and Falsification of Digital Files due to Quadratic Interpolation, Journal of KIISE, (2018), Vol.45, No.3 pp.311-320.
DOI: https://doi.org/10.5626/JOK.2018.45.3.31
[9] Heo Hee-Soo, So Byung-Min, Yang IL-Ho, and Yu Ha-Jin, A Speech Waveform Forgery Detection Algorithm Based on Frequency Distribution Analysis, Phonetics and Speech Sciences, (2015), Vol.7, No.4 pp.35-40.
DOI: http://dx.doi.org/10.13064/KSSS.2015.7
[10] https://sourceforge.net/projects/mp4-inspector/, Sept 11 (2023)
[11] Seo-Yeong Ahn, Se-Hui Ryu, Kyung-Wha Kim, and Ki-Hyung Hong, A comparative, A comparative analysis of metadata structures and attributes of Samsung smartphone voice recording files for forensic use, Phonetics and Speech Sciences, (2022), Vol.14, No.3, pp.103-112.
DOI: https://doi.org/10.13064/KSSS.2022.14.3.103
[12] Kyung-Wha Kim, A study on the forensic application of smartphone recording database, Journal of Digital Forensics, (2021), Vol.15, No.1, pp.26-42.
DOI: https://doi.org/10.22798/kdfs.2021.15.1.26
[13] YouJin Song, Gibum Kim, A Study on the Detection of Falsification of Voice Recording Files in an Application, Journal of Digital Forensics, (2022), Vol.16, No.3, pp.65-76.
DOI: https://doi.org/10.22798/kdfs.2022.16.3.65
[14] Il-Ho Yang, Kyung-Wha Kim, Myung-Jae Kim, Rock-Seon Baek, Hee-Soo Heo, Ha-Jin Yu, An Automatic Method of Detecting Audio Signal Tampering in Forensic Phonetics, Phonetics and Speech Sciences, (2014), Vol.6, No.2, pp.21-28.
DOI: https://doi.org/10.13064/ksss.2014.6.2.021.
[15] So-Jeong Eom, Hyun-Soo Kim, and Hae-Yeoun Lee, Audio Forensics for Smartphone Recording Detection using Deep Learning, The Journal of Korean Institute of Information Technology, (2022), Vol.20, No.7, pp.103-109.
DOI: https://doi.org/10.14801/jkiit.2022.20.7.103
[16] Youngjun Sim, Jungyu Choi, and Sungbin Im, Synthetic Speech Classification based on Cascade Connection of CNN and MKDE Models, Journal of The Institute of Electronics and Information Engineers, (2023), Vol.60, No.2, pp.94-101.
DOI: https://doi.org/10.5573/ieie.2023.60.2.94
[17] Seung-Woo Han, Seong-Hun Han, Seong-Min You, Dong-ho Song, and Chang-Jin Seo, Deep voice detection system based on voice feature extraction and deep learing, Prodeedings of the the 54th KIEE Summer Conference, Korea Institute of Electrical Engineers, (2023)
[18] Feng Liu, Tongsheng Shen, Zailei Luo, Dexin Zhao, and Shaojun Guo. Underwater Target Recognition Using Convolutional Recurrent Neural Networks with 3-D Mel-spectrogram and Data Augmentation, Applied Acoustics, (2021), Vol.178, 107989.
DOI: https://doi.org/10.1016/j.apacoust.2021.107989
[19] Jongwon Seok, Microphone Type Classification for Digital Audio Forgery Detection, Journal of Korea Multimedia Society, (2015), Vol. 18, No. 3, pp.323-329.
DOI: http://dx.doi.org/10.9717/kmms.2015.18.3.323
APA:
Son, Y. M., Park, J. W. (2023). Analysis of Voice File Forgery Detection Techniques: Focusing on Korean Academic Journals. Asia-pacific Journal of Convergent Research Interchange (APJCRI), ISSN: 2508-9080 (Print); 2671-5325 (Online), KCTRS, 9(11), 127-136. doi: 10.47116/apjcri.2023.11.12.
MLA:
Son, Yeongmin, et al. “Analysis of Voice File Forgery Detection Techniques: Focusing on Korean Academic Journals.” Asia-pacific Journal of Convergent Research Interchange, ISSN: 2508-9080 (Print); 2671-5325 (Online), KCTRS, vol. 9, no. 11, 2023, pp. 127-136. APJCRI, http://apjcriweb.org/content/vol9no11/12.html.
IEEE:
[1] Y. M. Son, J. W. Park, “Analysis of Voice File Forgery Detection Techniques: Focusing on Korean Academic Journals.” Asia-pacific Journal of Convergent Research Interchange (APJCRI), ISSN: 2508-9080 (Print); 2671-5325 (Online), KCTRS, vol. 9, no. 11, pp. 127-136, November 2023.