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20242026
most citedRawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection

1 citations · 1 across the 9 of their papers we have counts for

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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

Reject Threshold Adaptation for Open-Set Model Attribution of Deepfake Audio

Xinrui Yan, Jiangyan Yi, Jianhua Tao +6

Open environment oriented open set model attribution of deepfake audio is an emerging research topic, aiming to identify the generation models of deepfake audio. Most previous work…

cs.SD2024

Utilizing Speaker Profiles for Impersonation Audio Detection

Hao Gu, JiangYan Yi, Chenglong Wang +5

Fake audio detection is an emerging active topic. A growing number of literatures have aimed to detect fake utterance, which are mostly generated by Text-to-speech (TTS) or voice c…

cs.SD2024

An Unsupervised Domain Adaptation Method for Locating Manipulated Region in partially fake Audio

Siding Zeng, Jiangyan Yi, Jianhua Tao +4

When the task of locating manipulation regions in partially-fake audio (PFA) involves cross-domain datasets, the performance of deep learning models drops significantly due to the…

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…