Generation and Detection of Media Clones

Echizen, Isao; Babaguchi, Noboru; Yamagishi, Junichi; Nitta, Naoko; Nakashima, Yuta; Nakamura, Kazuaki; Kono, Ken; Fang, Fuming; Myojin, Seiko; Kuang, Zhenzhong; Nguyen, Huy H.; Tieu, Ngoc-Dung T.

doi:10.1587/transinf.2020mui0002

Cited by 4 publications

(1 citation statement)

References 59 publications

(80 reference statements)

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“…Today, technology has reached a level of maturity that enables biometric authentication across many different applications and devices. However, it is essential to make an effort to safeguard against identity fraud attempts, especially considering the increasing number of generated media as mentioned in Echizen et al (2021) . Specifically, automatic speaker verification (ASV) systems, which are frequently used for speaker verification in telephony, are prone to malicious authentication attempts since they rely solely on the received sound as the means of authentication.…”

Section: Introductionmentioning

confidence: 99%

Voice spoofing detection using a neural networks assembly considering spectrograms and mel frequency cepstral coefficients

Hernández-Nava,

Rincón-García,

Lara-Velázquez

et al. 2023

PeerJ Computer Science

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Nowadays, biometric authentication has gained relevance due to the technological advances that have allowed its inclusion in many daily-use devices. However, this same advantage has also brought dangers, as spoofing attacks are now more common. This work addresses the vulnerabilities of automatic speaker verification authentication systems, which are prone to attacks arising from new techniques for the generation of spoofed audio. In this article, we present a countermeasure for these attacks using an approach that includes easy to implement feature extractors such as spectrograms and mel frequency cepstral coefficients, as well as a modular architecture based on deep neural networks. Finally, we evaluate our proposal using the well-know ASVspoof 2017 V2 database, the experiments show that using the final architecture the best performance is obtained, achieving an equal error rate of 6.66% on the evaluation set.

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Section: Introductionmentioning

confidence: 99%

Voice spoofing detection using a neural networks assembly considering spectrograms and mel frequency cepstral coefficients

Hernández-Nava,

Rincón-García,

Lara-Velázquez

et al. 2023

PeerJ Computer Science

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Deepfakes: evolution and trends

et al. 2023

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This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.

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RealとFakeの狭間で

馬場口,

越前

2023

ISS JOURNAL

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Generation and Detection of Media Clones

Cited by 4 publications

References 59 publications

Voice spoofing detection using a neural networks assembly considering spectrograms and mel frequency cepstral coefficients

Voice spoofing detection using a neural networks assembly considering spectrograms and mel frequency cepstral coefficients

Deepfakes: evolution and trends

RealとFakeの狭間で

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