arxiv_cv 90% Match Research Paper Security researchers,Biometric system developers,AI researchers in generative models,Identity verification solution providers 2 months ago

ID-Card Synthetic Generation: Toward a Simulated Bona fide Dataset

computer-vision › diffusion-models

📄 Abstract

Abstract: Nowadays, the development of a Presentation Attack Detection (PAD) system for ID cards presents a challenge due to the lack of images available to train a robust PAD system and the increase in diversity of possible attack instrument species. Today, most algorithms focus on generating attack samples and do not take into account the limited number of bona fide images. This work is one of the first to propose a method for mimicking bona fide images by generating synthetic versions of them using Stable Diffusion, which may help improve the generalisation capabilities of the detector. Furthermore, the new images generated are evaluated in a system trained from scratch and in a commercial solution. The PAD system yields an interesting result, as it identifies our images as bona fide, which has a positive impact on detection performance and data restrictions.

Key Contributions

Proposes a novel method for generating synthetic bona fide ID card images using Stable Diffusion to address the scarcity of real data for training Presentation Attack Detection (PAD) systems. Evaluates the generated images in a PAD system, showing positive impact on detection performance and data restrictions.

Business Value

Enhances the robustness and accuracy of ID card verification systems by providing a method to generate diverse training data, leading to more secure and reliable identity checks.

Paper Metadata

Innovation Type

Methodology and Data Generation

Deployment Feasibility

High. The method leverages a well-established generative model (Stable Diffusion) and can be integrated into existing PAD system development pipelines.

Limitations Addressed

Limited number of bona fide images for training PAD systems, increasing diversity of attack instruments.

Performance Gains

Identifies generated images as bona fide, positively impacting detection performance and addressing data restrictions.

Technical Tags

Presentation Attack Detection (PAD)ID CardsSynthetic Data GenerationStable DiffusionBona fide imagesAttack samplesGeneralizationDeep LearningImage Synthesis

Research Topics

Adversarial Attacks and DefensesGenerative ModelsBiometric SecuritySynthetic Data GenerationDeep Learning for Security

Methods & Architectures

Synthetic image generationStable DiffusionTransfer learning (implied)Training from scratch Stable Diffusion

Applications & Tasks

Identity Verification Security Biometrics Document Security Lack of bona fide images for trainingDiversity of attack instrumentsLimited generalization of PAD systems Presentation Attack Detection (PAD)Generating synthetic bona fide ID card images

Related Fields

Computer VisionGenerative AISecurityBiometricsDeep Learning

Keywords

Presentation Attack DetectionPADID CardsSynthetic DataStable DiffusionBona fideAttack samplesGeneralizationDeep LearningImage SynthesisBiometricsSecurity

Academic Context

#Adversarial Attacks and Defenses#Generative Models#Biometric Security#Synthetic Data Generation#Deep Learning for Security

Technology Stack

Frameworks & Libraries

Stable Diffusion

Commercial Potential

Potential Products

Synthetic data generation services for security applicationsImproved PAD algorithms

Target Industries

BankingGovernmentTravelTechnology (Security)

Use Case Examples

Generating diverse training data for facial recognition systemsCreating realistic ID card samples for security testing

Competitive Edge

Addresses a critical data scarcity problem in PAD development by using advanced generative models, offering a more data-efficient approach than relying solely on real-world samples.

Market Opportunity

Large and growing market for identity verification and security solutions.

Revenue Models

Licensing of synthetic data generation technologyservices for creating custom datasets.

Resource Requirements

Compute Needs

Significant GPU resources for training and fine-tuning Stable Diffusion models.

Data Requirements

A dataset of real ID card images (both bona fide and attack samples) for initial training/fine-tuning and evaluation.

Deployment Constraints

Computational cost of generating high-quality synthetic images, potential for generated data to not perfectly capture all real-world variations.

Scalability

The generation process can be scaled by utilizing more compute resources.

Regulatory Considerations

Data privacy concerns if real ID card data is used for training.

Production Readiness

Maturity Level

Research Prototype

Time to Market

1-2 years

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