Authors:
Nicolas M. Müller, Karla Pizzi, Jennifer Williams
Where published:
Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia (DDAM ’22)
Published:
10 October, 2022
Dataset names (used for):
- ASVspoof 2019: A large-scale public database of synthesized, converted, and replayed speech
Some description of the approach:
The dataset is used in a gamified online experiment where participants distinguish between real and fake audio samples. It includes both bona-fide and deepfake audio samples, with users’ classifications and AI model predictions recorded.
The dataset is used in a gamified online experiment where participants distinguish between real and fake audio samples. It includes both bona-fide and deepfake audio samples, with users’ classifications and AI model predictions recorded.
Some description of the data:
The dataset includes 13,229 game rounds played by 410 unique users. It captures user demographics (IT experience, age, native language) and AI model predictions.
The dataset includes 13,229 game rounds played by 410 unique users. It captures user demographics (IT experience, age, native language) and AI model predictions.
Keywords:
Deepfake, Human perception, Audio spoofing
Instance Represent:
Audio samples classified by users and AI for authenticity
Dataset Characteristics:
13,229 game rounds, user demographics, and AI model predictions. Audio recordings are used, with user classifications and AI assessments for authenticity.
Subject Area:
Cybersecurity, Human-Computer Interaction
Associated Tools:
Deepfake detection, human vs. machine comparison
Feature Type:
Audio recordings, user demographics