20 citations · 22 across the 6 of their papers we have counts for
6 papers
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…
WMCodec: End-to-End Neural Speech Codec with Deep Watermarking for Authenticity Verification
Junzuo Zhou, Jiangyan Yi, Yong Ren +3
Recent advances in speech spoofing necessitate stronger verification mechanisms in neural speech codecs to ensure authenticity. Current methods embed numerical watermarks before co…
ADD 2023: Towards Audio Deepfake Detection and Analysis in the Wild
Jiangyan Yi, Chu Yuan Zhang, Jianhua Tao +5
The growing prominence of the field of audio deepfake detection is driven by its wide range of applications, notably in protecting the public from potential fraud and other malicio…
TraceableSpeech: Towards Proactively Traceable Text-to-Speech with Watermarking
Junzuo Zhou, Jiangyan Yi, Tao Wang +5
Various threats posed by the progress in text-to-speech (TTS) have prompted the need to reliably trace synthesized speech. However, contemporary approaches to this task involve add…
Fewer-token Neural Speech Codec with Time-invariant Codes
Yong Ren, Tao Wang, Jiangyan Yi +4
Language model based text-to-speech (TTS) models, like VALL-E, have gained attention for their outstanding in-context learning capability in zero-shot scenarios. Neural speech code…
ADD 2023: the Second Audio Deepfake Detection Challenge
Jiangyan Yi, Jianhua Tao, Ruibo Fu +15
Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the…