Deepfake Detection Challenge Dataset
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About
The Deepfake Detection Challenge Dataset serves as a benchmark for advancing deepfake detection models. Launched in September 2019 by Meta in collaboration with industry leaders and academic experts, it contains over 100,000 videos designed to assess and improve automated detection methods for manipulated media. The dataset has two versions: a Preview dataset with 5k videos and a Full dataset with 124k videos, both featuring various facial modification algorithms. Researchers are invited to use this dataset to explore new detection strategies. The initiative aims to foster learning and improve technology for recognizing harmful deepfakes, with research outputs available through associated papers. Users must create an AWS account to access the datasets, and references should be cited when used in research.
Platform
Task
Features
• contains over 100,000 videos for deepfake detection research
• collaborative benchmarking for ai models
• available in two versions: preview and full datasets
• includes associated research papers
• encourages citation for academic work
FAQs
What is the purpose of the Deepfake Detection Challenge Dataset?
The dataset is designed to accelerate research on deepfake detection technologies by providing a large and diverse dataset for testing models.
How can I access the dataset?
You can access the dataset by creating an AWS account and following the download instructions on the website.
What are the types of datasets available?
There are two types of datasets: the Preview dataset (5k videos) and the Full dataset (124k videos).
What are the prerequisites to download the datasets?
You need to have an AWS account and complete a setup including creating an IAM user and noting your AWS account ID.
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