SynID: Passport Synthetic Dataset for Presentation Attack Detection

• URL: http://arxiv.org/abs/2505.07540v1
• Date: Mon, 12 May 2025 13:24:54 GMT
• Title: SynID: Passport Synthetic Dataset for Presentation Attack Detection
• Authors: Juan E. Tapia, Fabian Stockhardt, Lázaro Janier González-Soler, Christoph Busch,
• Abstract summary: Increase is driven by several factors, including the rise of remote work, online purchasing, migration, and advancements in synthetic images.<n>This work proposes a new passport dataset generated from a hybrid method that combines synthetic data and open-access information.
• Score: 7.1212970088491385
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• LLM for Barcodes: Generating Diverse Synthetic Data for Identity Documents [2.697503433221448]
  We introduce a new approach to synthetic data generation that uses LLMs to create contextually rich and realistic data without relying on predefined field.<n>Our approach simplifies the process of dataset creation, eliminating the need for extensive domain knowledge.<n>This scalable, privacy-first solution is a big step forward in advancing machine learning for automated document processing and identity verification.
  arXiv  Detail & Related papers  (2024-11-22T14:21:18Z)
• IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection [25.980165854663145]
  IDNet is a benchmark dataset designed to advance privacy-preserving fraud detection efforts. It comprises 837,060 images of synthetically generated identity documents, totaling approximately 490 gigabytes. We evaluate the utility and present use cases of the dataset, illustrating how it can aid in training privacy-preserving fraud detection methods.
  arXiv  Detail & Related papers  (2024-08-03T07:05:40Z)
• Keypoint Promptable Re-Identification [76.31113049256375]
  Occluded Person Re-Identification (ReID) is a metric learning task that involves matching occluded individuals based on their appearance. We introduce Keypoint Promptable ReID (KPR), a novel formulation of the ReID problem that explicitly complements the input bounding box with a set of semantic keypoints. We release custom keypoint labels for four popular ReID benchmarks. Experiments on person retrieval, but also on pose tracking, demonstrate that our method systematically surpasses previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-07-25T15:20:58Z)
• Synthetic dataset of ID and Travel Document [1.9296797946506603]
  This paper presents a new synthetic dataset of ID and travel documents, called SIDTD. The SIDTD dataset is created to help training and evaluating forged ID documents detection systems.
  arXiv  Detail & Related papers  (2024-01-03T18:06:28Z)
• Open-Set: ID Card Presentation Attack Detection using Neural Transfer Style [2.946386240942919]
  This work explores ID card Presentation Attack Instruments (PAI) in order to improve the generation of samples with four Generative Adversarial Networks (GANs) based image translation models. We obtain an EER performance increase of 0.63% points for print attacks and a loss of 0.29% for screen capture attacks.
  arXiv  Detail & Related papers  (2023-12-21T16:28:08Z)
• PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding [102.07914175196817]
  PhotoMaker is an efficient personalized text-to-image generation method. It encodes an arbitrary number of input ID images into a stack ID embedding for preserving ID information.
  arXiv  Detail & Related papers  (2023-12-07T17:32:29Z)
• Synthetic ID Card Image Generation for Improving Presentation Attack Detection [12.232059909207578]
  This work explores three methods for synthetically generating ID card images to increase the amount of data while training fraud-detection networks. Our results indicate that databases can be supplemented with synthetic images without any loss in performance for the print/scan Presentation Attack Instrument Species (PAIS) and a loss in performance of 1% for the screen capture PAIS.
  arXiv  Detail & Related papers  (2022-10-31T19:07:30Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)
• MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis [48.35030471041193]
  MIDV-2020 consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents. With 72409 annotated images in total, to the date of publication the proposed dataset is the largest publicly available identity documents dataset.
  arXiv  Detail & Related papers  (2021-07-01T12:14:17Z)
• Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis [69.09526348527203]
  Deep generative models have led to highly realistic media, known as deepfakes, that are commonly indistinguishable from real to human eyes. We propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios.
  arXiv  Detail & Related papers  (2021-05-29T21:22:24Z)
• Towards Precise Intra-camera Supervised Person Re-identification [54.86892428155225]
  Intra-camera supervision (ICS) for person re-identification (Re-ID) assumes that identity labels are independently annotated within each camera view. Lack of inter-camera labels makes the ICS Re-ID problem much more challenging than the fully supervised counterpart. Our approach performs even comparable to state-of-the-art fully supervised methods in two of the datasets.
  arXiv  Detail & Related papers  (2020-02-12T11:56:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.