4 papers
ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection
Hao Gu, Jiangyan Yi, Chenglong Wang +6
Audio deepfake detection (ADD) has grown increasingly important due to the rise of high-fidelity audio generative models and their potential for misuse. Given that audio large lang…
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…
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…
Audio Deepfake Attribution: An Initial Dataset and Investigation
Xinrui Yan, Jiangyan Yi, Jianhua Tao +1
The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious manipulation of content. This has led to an increase in studies aimed…