--- title: "Anti-Spoofing Replay PC Videos Dataset" description: "High-quality replay attack dataset containing 4,714 PC-recorded video clips of real faces, designed for training and evaluating face recognition and liveness detection systems. This anti-spoofing…" url: "https://unidata.pro/datasets/anti-spoofing-replay-pc-videos/" date_modified: "2025-12-25T10:19:20+03:00" language: "en-US" --- High-quality replay attack dataset containing 4,714 PC-recorded video clips of real faces, designed for training and evaluating face recognition and liveness detection systems. This anti-spoofing videos dataset includes diverse attack scenarios, technical metadata (age, gender, ethnicity), and MP4/MOV formats to support spoofing detection, biometric security, and computer vision model development. ## Dataset Structure ### The Numbers Section **Numbered list:** - **Number:** 4,714 — **Text:** Videos - **Number:** 4,714 — **Text:** People ### Tooltips Section **Tooltip items:** - **Name:** Facial Recognition - **Name:** Liveness Detection - **Name:** Security - **Name:** Anti-spoofing - **Name:** Computer Vision ### Dataset Information **Table with data:** | Characteristic | Data | | --- | --- | | Description | Live videos of people for anti-spoofing tasks | | Data types | Video | | Tasks | Face recognition, Face detection | | Number of people | 4714 | | Number of videos | 4714 | | Labeling | Only technical characteristics and metadata (age, gender, ethnicity) | | Gender | Male, Female | **Media Slider:** - **Video on Slayder:** - **Video on Slayder:** **Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1ILdrbc0wGtbKRZk4hg7yGgkjRV1aLND_) ### Technical Specifications **Table with data:** | Characteristic | Data | | --- | --- | | Video extension | MP4, MOV | **Source and data collection methodology:** Source and collection methodology: Data was collected via crowdsourcing platforms. ### Dataset Use Cases - Slider **Industry Cards:** - **Industry:** Biometric Security Systems — **Title:** Replay attack detection for identity verification — **Text:** This anti-spoofing PC Videos Dataset supports the development of biometric security systems by providing replay attack videos captured on computers. The replay attack dataset enables training detection algorithms to distinguish real faces from replay videos, improving spoofing detection, facial liveness checks, and identity verification in real-world biometric recognition workflows. - **Industry:** Financial Technology (FinTech) — **Title:** Secure remote onboarding and authentication — **Text:** FinTech platforms use this anti-spoofing dataset to strengthen face recognition during remote customer onboarding. It consists of PC-based video recordings that help systems detect spoofing attempts, replay attacks, and fake faces, enhancing biometric authentication, fraud prevention, and compliance with digital identity verification requirements. - **Industry:** Computer Vision and AI Research — **Title:** Benchmarking face antispoofing models — **Text:** Researchers apply such datasets as reliable training data for computer vision models focused on face antispoofing. With diverse video clips and labeled metadata, it supports detection tasks, algorithm benchmarking, and performance evaluation across different attack scenarios and device types in recognition technology research. - **Industry:** Enterprise Security Technology — **Title:** Liveness detection for access control systems — **Text:** Security technology providers use this PC Videos Dataset to improve liveness detection in access control and surveillance systems. It supports anti-spoofing solutions by exposing models to replay attacks and spoofing techniques, enabling accurate detection of fake users and stronger protection across enterprise security systems. ### Fact **FAQs Heading:** FAQs **List of Questions:** - **Question:** Can I request a sample of the anti-spoofing PC videos dataset before purchasing? — **Answer:** Yes. Unidata provides free samples so you can assess video quality, metadata structure, and suitability for spoofing detection and face recognition tasks. - **Question:** What types of annotations are provided with this dataset? — **Answer:** The dataset includes technical metadata such as age, gender, and ethnicity. These annotations enable demographic analysis while supporting privacy-aware biometric model training. - **Question:** What video formats and technical characteristics are available? — **Answer:** All video clips are provided in MP4 and MOV formats. The recordings are compatible with common anti-spoofing systems, detection algorithms, and recognition technology pipelines. - **Question:** How is the anti-spoofing replay PC video data collected? — **Answer:** Data was collected through crowdsourcing platforms using PC-based recording setups. This approach ensures realistic replay attack scenarios for training biometric security and detection systems. - **Question:** How are Unidata datasets licensed? — **Answer:** Unidata follows a dual-licensing model. Free samples are available for evaluation, while the complete anti-spoofing videos dataset is provided through purchase. - **Question:** Do Unidata datasets comply with GDPR and data privacy regulations? — **Answer:** Yes. All datasets comply with GDPR and applicable data protection laws. Data is sourced from legally permissible channels to ensure ethical use in biometric systems. - **Question:** How are Unidata datasets stored and secured? — **Answer:** Datasets are stored on AWS cloud infrastructure with controls aligned to ISO 27001 and ISO 27701 standards. This ensures secure storage, high availability, and privacy-focused data management. - **Question:** Why is demographic diversity important in replay attack datasets? — **Answer:** The dataset includes participants of different ages, genders, and ethnic backgrounds, helping developers evaluate model performance across diverse populations. This diversity supports the development of more reliable and unbiased biometric security and liveness detection systems. - **Question:** What makes this replay attack dataset valuable for biometric security? — **Answer:** Unlike generic face video collections, this dataset focuses specifically on replay attack scenarios that target face recognition systems. Its combination of authentic participants, demographic metadata, and standardized video formats makes it useful for developing production-ready anti-spoofing solutions. [Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)