扩散模型在指纹生成与指纹保护中的应用进展

This paper reviews recent progress in fingerprint image generation using deep generative models, with a focus on Generative Adversarial Network (GAN)-based and diffusion-based approaches. Fingerprint data are essential for biometric recognition, but collecting large and diverse datasets is difficult, especially for latent fingerprints. Early methods based on physical or statistical modeling could not produce realistic textures or sufficient diversity. With the development of deep learning, GAN-based models such as FingerGAN, PrintsGAN, and lightweight GANs have significantly improved the realism of generated fingerprints by learning data distributions directly. These methods introduce techniques such as structural constraints, multi-stage generation, and improved loss functions to enhance image quality and stability. However, GAN-based models still suffer from problems such as training instability, mode collapse, and limited control over identity consistency. To address these issues, diffusion models have recently been introduced into fingerprint generation. By gradually denoising random noise, diffusion models can generate high-quality and diverse fingerprint images with better stability. Advanced diffusion frameworks further enable controllable generation, allowing users to adjust fingerprint attributes such as style, quality, and sensor type while preserving identity information. Overall, diffusion-based methods show strong potential to become the next generation of fingerprint synthesis techniques.

… 7] uses direction field generation and density field generation to … ridge pattern generation model to reconstruct real fingerprint … In the aforementioned diffusion model, a pivotal aspect lies …

… diffusion models enable highly realistic fingerprint forgeries, posing serious security threats to fingerprint … on a specific generator, is a critical requirement for practical fingerprint forgery …

Fingerprints have been crucial evidence for law enforcement agencies for a long time. Though the rapidly developing deep learning has dramatically improved the performance of the latent fingerprint recognition algorithm, a fully automated latent fingerprint identification system is still far from meeting actual needs. One major issue is the lack of publicly available latent fingerprint databases. Recently, diffusion probabilistic models have emerged as state-of-the-art deep generative methods for image synthesis. These models have better distribution coverage and less mode collapse than the popular Generative Adversarial Networks. In this paper, we propose an end-to-end latent fingerprint synthetic approach based on the improved denoising diffusion probabilistic model. The proposed approach could simultaneously generate latent, rolled, and plain fingerprints of high visual realism. Several primary degradation factors, such as various background textures, limited area of ridge patterns, and structural noise, can be directly generated without any postprocessing, unlike existing methods. We conduct NFIQ2 and perceptual analysis in the experiments to evaluate the proposed approach. The results indicate that the quality and visual realism of the proposed synthetic fingerprints is similar to the natural ones, demonstrating the effectiveness of our approach.

The advancement of fingerprint research within public academic circles has been trailing behind facial recognition, primarily due to the scarcity of extensive publicly available datasets, despite fingerprints being widely used across various domains. Recent progress has seen the application of deep learning techniques to synthesize fingerprints, predominantly focusing on large-area fingerprints within existing datasets. However, with the emergence of AIoT and edge devices, the importance of tiny partial fingerprints has been underscored for their faster and more cost-effective properties. Yet, there remains a lack of publicly accessible datasets for such fingerprints. To address this issue, we introduce publicly available datasets tailored for tiny partial fingerprints. Using advanced generative deep learning, we pioneer diffusion methods for fingerprint synthesis. By combining random sampling with inpainting diffusion guided by feature keypoints masks, we enhance data augmentation while preserving key features, achieving up to 99.1% recognition matching rate. To demonstrate the usefulness of our fingerprint images generated using our approach, we conducted experiments involving model training for various tasks, including denoising, deblurring, and deep forgery detection. The results showed that models trained with our generated datasets outperformed those trained without our datasets or with other synthetic datasets. This indicates that our approach not only produces diverse fingerprints but also improves the model’s generalization capabilities. Furthermore, our approach ensures confidentiality without compromise by partially transforming randomly sampled synthetic fingerprints, which reduces the likelihood of real fingerprints being leaked. The total number of generated fingerprints published in this article amounts to 818,077. Moving forward, we are ongoing updates and releases to contribute to the advancement of the tiny partial fingerprint field. The code and our generated tiny partial fingerprint dataset can be accessed at https://github.com/Hsu0623/Inpainting-Diffusion-Synthetic-and-Data-Augment-with-Feature-Keypoints-for-Tiny-Partial-Fingerprints.git

… An example is DDPM [22], which uses diffusion probabilistic … model is used for image synthesis, which makes it difficult to … Based on the additional loss term using the latent feature …

High-quality, diversified, and large-scale datasets are crucial for creating reliable deep-learning models for biometric applications. Unfortunately, there is a shortage of well-labeled data. This paper introduces a text-conditional biometric imaging generation framework, addressing the complexities associated with multi-modality considerations. The proposed framework harnesses cutting-edge diffusion probabilistic models to produce multi-modal biometric images at high resolutions, seamlessly aligning with biometric language prompts. The experimental results unequivocally validate the efficacy of the proposed framework in generating a diverse array of highly realistic synthetic biometric images while consistently maintaining a commendable level of fidelity when juxtaposed with their respective reference datasets. The contributions of this study offer substantial potential for propelling advancements in biometric imaging research.

… As a result, the present paper opens opportunities suggesting potential fingerprinting tools … and AI in latent fingerprinting in doublefold manner, i)synthetic fingerprints data generation …

… images showing that a DDPM model clearly can generate realistic fingerprint images. … Specifically, we focus on generating fingerprint patches and transforming fingerprints using …

The majority of contemporary fingerprint synthesis is based on the Generative Adversarial Network (GAN). Recently, the Denoising Diffusion Probabilistic Model (DDPM) has been demonstrated to be more effective than GAN in numerous scenarios, particularly in terms of diversity and fidelity. This research develops a model based on the enhanced DDPM for fingerprint generation. Specifically, the image is decomposed into sub-images of varying frequency sub-bands through the use of a wavelet packet transform (WPT). This method enables DDPM to operate at a more local and detailed level, thereby accurately obtaining the characteristics of the data. Furthermore, a polynomial noise schedule has been designed to replace the linear noise strategy, which can result in a smoother noise addition process. Experiments based on multiple metrics on the datasets SOCOFing and NIST4 demonstrate that the proposed model is superior to existing models.

Optical coherence tomography (OCT) is renowned for its high resolution and ability to capture the 3D structure of fingertip skin, significantly enhancing the anticounterfeiting capabilities of fingerprint recognition systems. However, the scarcity of OCT fingerprint datasets, exacerbated by data collection challenges and privacy concerns, poses a major hurdle for practical implementation. We propose a novel conditional diffusion model that generates highly realistic OCT fingerprints from segmentation masks, marking the first attempt to synthesize such images. By modifying the noise model in the diffusion process to account for speckle noise, our method achieves accurate noise simulation and effective removal, resulting in clearer detail feature generation. Subjective evaluations and multiple objective metrics confirm the superior visual quality and diversity of the generated images. By incorporating these images into training datasets for presentation attack detection (PAD) and fingerprint layer segmentation tasks, our method achieves pixel distributions highly consistent with bona fide fingerprints and learns detailed skin structures through segmentation mask guidance. These results highlight the potential of our approach to enhance the performance of OCT fingerprints in practical applications.

The development of intelligent logistics increasingly leads the evolution of future supply chain technology. As an innovative technology in the field of logistics, Unmanned Aerial Vehicles (UAVs) provide efficient, fast and flexible solutions for transportation and delivery. However, the application of UAVs needs to ensure effective identity authentication and the security of the delivery process. Because biometric data (such as fingerprints, facial recognition, iris scans) is highly sensitive personal information. Once stolen or abused, it may lead to serious personal privacy disclosure problems. In this paper, we use differential privacy and diffusion models to implement secure face recognition and identity authentication in edge computing environments for address the privacy issues in UAV delivery. The UAVs collect the user’s biometric data through edge computing nodes during delivery, and uses a diffusion model for secure transmission to protect user privacy. The edge computing node at the receiving end performs face recognition and authentication to ensure that only legitimate users can accept the delivery. Our study not only improves the accuracy of user identity authentication, but also protects the privacy of users.

Generation of synthetic biometric samples such as, for instance, fingerprint images gains more and more importance especially in view of recent cross-border regulations on security of private data. The reason is that biometric data is designated in recent regulations such as the EU GDPR as a special category of private data, making sharing datasets of biometric samples hardly possible even for research purposes. The usage of fingerprint images in forensic research faces the same challenge. The replacement of real datasets by synthetic datasets is the most advantageous straightforward solution which bears, however, the risk of generating "unrealistic" samples or "unrealistic distributions" of samples which may visually appear realistic. Despite numerous efforts to generate high-quality fingerprints, there is still no common agreement on how to define "high-quality'' and how to validate that generated samples are realistic enough. Here, we propose general requirements on synthetic biometric samples (that are also applicable for fingerprint images used in forensic application scenarios) together with formal metrics to validate whether the requirements are fulfilled. Validation of our proposed requirements enables establishing the quality of a generative model (informed evaluation) or even the quality of a dataset of generated samples (blind evaluation). Moreover, we demonstrate in an example how our proposed evaluation concept can be applied to a comparison of real and synthetic datasets aiming at revealing if the synthetic samples exhibit significantly different properties as compared to real ones.

… consider generative models for forensic … fingerprint representation. The results provide a much stronger argument for the individuality of fingerprints in forensics than previous generative …