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.gitattributes

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2D Printed Mask Attacks and Replay Attacks Videos Dataset.csv

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+id,type_attack,gender
+1,in_car_videos_and_fakes,Male
+2,in_car_videos_and_fakes,Male
+1,toloka_video_fakes,Female
+2,toloka_video_fakes,Male
+3,toloka_video_fakes,Female
+1,tolokers_based_spoofs,Female
+2,tolokers_based_spoofs,Male
+3,tolokers_based_spoofs,Male
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+1,youdo_heads,Female
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README.md

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+---
+license: cc-by-nc-nd-4.0
+task_categories:
+- video-classification
+tags:
+- Computer Vision
+- Machine learning
+- Security
+- Anti-spoofing
+- Liveness Detection
+size_categories:
+- 10K<n<100K
+---
+# 2D Mask Attack Dataset - 26 436 videos
+The dataset comprises **26,436** videos of real faces, **2D print attacks** (printed photos), and **replay attacks** (faces displayed on screens), captured under varied conditions. Designed for **attack detection** research, it supports the development of robust **face antispoofing** and **spoofing detection** methods, critical for **facial recognition security**.
+
+Ideal for training models and refining **anti-spoofing** methods, the dataset enhances **detection accuracy** in **biometric systems**. - **[Get the data](https://unidata.pro/datasets/2d-printed-mask-and-replay-attack-videos-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=2d-printed-mask-dataset)**
+
+# Frequently Asked Questions
+
+## Who can benefit from this 2D printed masks dataset?
+
+The 2D printed masks dataset is valuable for biometric security vendors, AI researchers, financial institutions, mobile authentication developers, and academic laboratories working on presentation attack detection.
+
+## What types of presentation attacks are included in this face anti-spoofing dataset?
+
+This face anti-spoofing dataset includes multiple presentation attack types designed to simulate real-world biometric fraud scenarios. Researchers can work with both 2D print attacks and silicone mask attacks, allowing models to learn how different spoofing techniques affect facial recognition systems. The dataset also includes images and videos featuring additional appearance variations such as wigs, glasses, facial hair, face masks, and different clothing styles.
+
+## How was the data collected for this presentation attack dataset?
+
+The dataset was collected by a UniData partner using a structured acquisition process designed specifically for biometric security research.
+
+## Example of the data 
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F27063537%2F9c0b06aa82909632bd83a7048ac513ae%2FFrame%2052%20(2).png?generation=1755454141939894&alt=media)
+
+## 💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at [https://unidata.pro](https://unidata.pro/datasets/2d-printed-mask-and-replay-attack-videos-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=2d-printed-mask-dataset) to discuss your requirements and pricing options.
+
+Researchers can leverage this training data to improve detection accuracy, validate models trained on adversarial examples, and advance recognition systems against sophisticated masked attacks.
+
+## 🌐 [UniData](https://unidata.pro/datasets/2d-printed-mask-and-replay-attack-videos-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=2d-printed-mask-dataset) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

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