Search Results for author:
Found 7 papers, 2 papers with code
Region-Based Optimization in Continual Learning for Audio Deepfake Detection
To address this issue, we propose a continual learning method named Region-Based Optimization (RegO) for audio deepfake detection.
Reject Threshold Adaptation for Open-Set Model Attribution of Deepfake Audio
To address the issues, we propose a novel framework for open set model attribution of deepfake audio with rejection threshold adaptation (ReTA).
ADD 2023: Towards Audio Deepfake Detection and Analysis in the Wild
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 malicious activities, prompting the need for greater attention and research in this area.
A Survey on Multimodal Large Language Models for Autonomous Driving
We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving.
Audio Deepfake Attribution: An Initial Dataset and Investigation
To address the challenges of attribution of continuously emerging unknown audio generation tools in the real world, we propose the Class-Representation Multi-Center Learning (CRML) method for open-set audio deepfake attribution (OSADA).
An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio
Many effective attempts have been made for fake audio detection.
ADD 2022: the First Audio Deep Synthesis Detection Challenge
Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021.
