mlsanityData to 7 Oct 2026

SynID: Passport Synthetic Dataset for Presentation Attack Detection

arXiv 2505.07540 ↗First posted 12 May 2025cs.CVcited by 4

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Abstract (arXiv)

The demand for Presentation Attack Detection (PAD) to identify fraudulent ID documents in remote verification systems has significantly risen in recent years. This increase is driven by several factors, including the rise of remote work, online purchasing, migration, and advancements in synthetic images. Additionally, we have noticed a surge in the number of attacks aimed at the enrolment process. Training a PAD to detect fake ID documents is very challenging because of the limited number of ID documents available due to privacy concerns. This work proposes a new passport dataset generated from a hybrid method that combines synthetic data and open-access information using the ICAO requirement to obtain realistic training and testing images.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 12 May 2025.

Builds on

8 of 16 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Densely Connected Convolutional Networkscited by 6,691
  3. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
  4. 2018MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Streamcited by 33
  5. 2021MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysiscited by 31
  6. 2022Synthetic ID Card Image Generation for Improving Presentation Attack Detectioncited by 18
  7. 2024First Competition on Presentation Attack Detection on ID Cardcited by 18
  8. 2024ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Datasetcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.