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DeepFake Videos Dataset

The deepfake dataset contains real and AI-generated deepfake videos, featuring diverse subjects with detailed metadata on age, gender, and ethnicity to help train powerful deepfake detectors

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  • files
    10,000+
  • people
    7,000+
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Data generation
  • Security

The deepfake dataset contains real and AI-generated deepfake videos, featuring diverse subjects with detailed metadata on age, gender, and ethnicity to help train powerful deepfake detectors

Get in touch Download sample
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Data generation
  • Security
  • files
    10,000+
  • people
    7,000+

Dataset Info

Characteristic Data
Description Real video of people with AI-generated faces, where individuals turn their heads in different directions
Data types Video
Tasks Facial recognition, Computer Vision
Total number of files 10,000
Number of people 7,000
Video generation sites aisaver.io, faceswapvideo.ai, magichour.ai
Labeling Metadata (age, gender, ethnicity)
Gender Male, Female
Ethnicity Asian (30%), African (70%)
Age Min = 18, max = 80, mean = 45
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Statistics

Distribution by age
Duration of the video duration
Distribution by gender
Distribution by ethnicity

Technical
Characteristics

Characteristic Data
Video extension mp4, MOV
Video Resolutions 1920 x 1080p, 480 x 360p, 1280 x 720p, 720 x 480p, 640 x 480p, 1920 x 920p
Video duration Mean = 9, median = 9, min = 2, max = 34
Frames per second Mean = 26.6
Devices iPhone 13 (30%), Google Pixel (70%)
Source and collection methodology. Data was collected by overlaying generated faces onto real videos using the following websites: aisaver.io, faceswapvideo.ai, and magichour.ai.

Dataset Use Cases

  • Cybersecurity & Digital Forensics

    Detecting Deepfake Content and Fake Videos

    Deepfake Videos Dataset provides critical training data for developing deepfake detectors and detection algorithms. Containing both real videos and fake videos generated using advanced deepfake technology, it allows analysts to train detection systems that identify synthetic media and protect against identity fraud, misinformation, and other digital threats.



  • AI & Machine Learning Research

    Training Models for Deepfake Detection

    This deep fake detection dataset is widely used in machine learning and deep learning projects. The dataset consists of thousands of video clips and face images, including manually labelled examples. Models trained on this data achieve better accuracy in spotting AI-generated videos and distinguishing between real and synthetic video datasets.



  • Media & Journalism

    Verifying Video Content Authenticity

    News organizations use such datasets to enhance video detection tools that verify YouTube videos, interviews, and shared clips. By training recognition systems on datasets containing both source videos and generated faces, journalists can validate footage, identify manipulated content, and strengthen trust in digital reporting.



  • Technology & App Development

    Building Safer Recognition and Verification Systems

    Tech companies rely on the synthetic video dataset to test facial recognition and object detection systems against deepfake content. The dataset comprising high-quality video frames, fake images, and synthetic data helps in creating more reliable generative AI detection methods. This improves authentication solutions and delivers better results in protecting digital platforms.



FAQs

How large is DeepFake Videos Dataset compared to other available datasets?
With 10,000 video clips and diverse demographic coverage, this collection is one of the largest datasets of its kind. Its scale allows models trained on it to achieve higher accuracy in deepfake content detection and facial recognition tasks.
What devices and resolutions are represented in the dataset?
Videos were recorded on iPhone 13 devices (30%) and Google Pixel devices (70%), then processed with deepfake technology. The dataset covers multiple resolutions, including 1080p, 720p, and 480p, supporting a wide range of video detection methods.
How was the data collected?
The dataset was built by generating fake faces using AI models and overlaying them on real videos (using the following tools: aisaver.io, faceswapvideo.ai, and magichour.ai).
Is it possible to request a custom deepfake dataset?
Custom datasets can be created on request, allowing you to specify generation methods, annotation formats, or target demographics. This flexibility ensures better results for applications such as face recognition, synthetic video detection, or generative AI model training.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model. Free samples are available for testing and evaluation, while full datasets, including the Deepfake Videos Dataset, can be accessed exclusively through purchase.
How are Unidata datasets stored?
Unidata securely stores datasets on AWS cloud infrastructure, ensuring high availability and scalability. Our storage practices comply with ISO 27001 and ISO 27701 standards, guaranteeing internationally recognized information security and privacy management for sensitive data.
How long does it take to receive the dataset?
Once you submit a request, our team will contact you to confirm details and finalize documentation. After signing and payment, the dataset will be delivered within 3–10 business days.
Is this a real-world dataset or synthetic data?
The Deepfake Videos Dataset is a synthetic video dataset created by combining real-world source videos with AI-generated faces. This hybrid approach provides realistic training material for developing deepfake detection methods, recognition models, and generative AI research.
Still have questions about using Unidata datasets? Read our user-guides

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