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20222026
most citedAudio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features

40 citations · 68 across the 11 of their papers we have counts for

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8 papers · 1 filter

cs.SD2026

RTCFake: Speech Deepfake Detection in Real-Time Communication

Jun Xue, Zhuolin Yi, Yihuan Huang +6

With the rapid advancement of speech generation technologies, the threat posed by speech deepfakes in real-time communication (RTC) scenarios has intensified. However, existing det…

cs.SD2024

Region-Based Optimization in Continual Learning for Audio Deepfake Detection

Yujie Chen, Jiangyan Yi, Cunhang Fan +10

Rapid advancements in speech synthesis and voice conversion bring convenience but also new security risks, creating an urgent need for effective audio deepfake detection. Although…

cs.SD2024★ 1 cited

RawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection

Yujie Chen, Jiangyan Yi, Jun Xue +7

Fake artefacts for discriminating between bonafide and fake audio can exist in both short- and long-range segments. Therefore, combining local and global feature information can ef…

cs.SD2024

Frequency-mix Knowledge Distillation for Fake Speech Detection

Cunhang Fan, Shunbo Dong, Jun Xue +3

In the telephony scenarios, the fake speech detection (FSD) task to combat speech spoofing attacks is challenging. Data augmentation (DA) methods are considered effective means to…

cs.SD2023★ 17 cited

Spatial Reconstructed Local Attention Res2Net with F0 Subband for Fake Speech Detection

Cunhang Fan, Jun Xue, Jianhua Tao +4

The rhythm of bonafide speech is often difficult to replicate, which causes that the fundamental frequency (F0) of synthetic speech is significantly different from that of real spe…

cs.SD2023★ 1 cited

Multi-perspective Information Fusion Res2Net with RandomSpecmix for Fake Speech Detection

Shunbo Dong, Jun Xue, Cunhang Fan +3

In this paper, we propose the multi-perspective information fusion (MPIF) Res2Net with random Specmix for fake speech detection (FSD). The main purpose of this system is to improve…

cs.SD2023★ 1 cited

Learning From Yourself: A Self-Distillation Method for Fake Speech Detection

Jun Xue, Cunhang Fan, Jiangyan Yi +4

In this paper, we propose a novel self-distillation method for fake speech detection (FSD), which can significantly improve the performance of FSD without increasing the model comp…

cs.SD2022★ 40 cited

Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features

Jun Xue, Cunhang Fan, Zhao Lv +6

Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients,…