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Document Liveness Challenge Dataset (DLC-2021).

Dmitry V Polevoy1,2,3, Irina V Sigareva1,4, Daria M Ershova1,5

  • 1Smart Engines Service LLC, 117312 Moscow, Russia.

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Summary
This summary is machine-generated.

A new dataset, DLC-2021, addresses the lack of public data for mobile identity document analysis. It includes diverse video clips for ID forensics, aiding fraud prevention in digital services.

Keywords:
document analysisdocument anti-frauddocument forgery detectiondocument recognitionidentity documentsliveness detectionmobile recognitionopen datascreen recapture detection

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Area of Science:

  • Computer Science
  • Forensic Science
  • Information Security

Background:

  • Mobile identity document analysis is crucial for e-government, fintech, and banking services.
  • Existing research faces challenges due to a lack of publicly available, secure datasets.
  • Current systems require robust performance in unconstrained environments for data extraction and fraud detection.

Purpose of the Study:

  • Introduce the DLC-2021 dataset, a novel resource for mobile identity document forensics.
  • Facilitate research in ID document analysis, particularly for fraud prevention and data extraction.
  • Provide benchmark baselines for critical tasks within the dataset.

Main Methods:

  • Collected 1424 video clips under diverse real-world conditions.
  • Included various formats: laminated/unlaminated mock IDs, grayscale copies, and screen recaptures.
  • Ensured GDPR compliance with synthetic IDs and artificial personal data.

Main Results:

  • The DLC-2021 dataset offers diverse scenarios for ID document analysis.
  • Benchmark results are provided for screen recapture and glare detection.
  • The dataset is openly available on Zenodo for public access.

Conclusions:

  • The DLC-2021 dataset significantly enhances research capabilities in mobile ID document analysis.
  • It supports the development of more secure and reliable identity verification systems.
  • Open access promotes wider adoption and advancement in the field of digital forensics.