UniDataPro/deepfake-videos-dataset

DeepFake Videos for detection tasks

Dataset consists of 10,000+ files featuring 7,000+ people, providing a comprehensive resource for research in deepfake detection and deepfake technology. It includes real videos of individuals with AI-generated faces overlaid, specifically designed to enhance liveness detection systems.

By utilizing this dataset, researchers can advance their understanding of deepfake generation and improve the performance of detection methods. - Get the data

Videos featuring different individuals, backgrounds, and scenarios, making it suitable for various research applications.

Metadata for the dataset

Dataset was created by generating fake faces and overlaying them onto authentic video clips sourced from platforms such as aisaver.io, faceswapvideo.ai, and magichour.ai.

Frequently Asked Questions

How were the deepfake videos created?

The deepfake videos were created by overlaying AI-generated faces onto real video recordings. The face-generation and replacement process used three online platforms: aisaver.io, faceswapvideo.ai, and magichour.ai.

What video resolutions are included in the deepfake dataset?

The deepfake dataset includes several video resolutions, ranging from lower-resolution 480 × 360p footage to Full HD 1920 × 1080p recordings. Other available resolutions include 1280 × 720p, 720 × 480p, 640 × 480p, and 1920 × 920p. This variation allows researchers to test how deepfake detection performance changes as spatial image quality changes.

Which devices were used to record the source videos?

The source recordings were captured using iPhone 13 devices and Google Pixel smartphones. Google Pixel recordings account for approximately 70% of the data, while iPhone 13 recordings account for approximately 30%. This device variation can help researchers evaluate whether a deepfake detector is sensitive to a particular smartphone camera pipeline.

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Researchers can leverage this dataset to enhance their understanding of deepfake detection and contribute to the development of more robust detection methods that can effectively combat the challenges posed by deepfake technology.

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