High Fidelity Fingerprint Generation: Quality, Uniqueness, and Privacy

• URL: http://arxiv.org/abs/2105.10403v1
• Date: Fri, 21 May 2021 15:18:28 GMT
• Title: High Fidelity Fingerprint Generation: Quality, Uniqueness, and Privacy
• Authors: Keivan Bahmani, Richard Plesh, Peter Johnson, Stephanie Schuckers, Timothy Swyka
• Abstract summary: We utilize progressive growth-based Generative Adversarial Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG) We demonstrate that the CFG is capable of generating realistic, high fidelity, $512times512$ pixels, full, plain impression fingerprints. Our results suggest that the fingerprints generated by the CFG are unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality.
• Score: 1.3911984813936993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we utilize progressive growth-based Generative Adversarial Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG). We demonstrate that the CFG is capable of generating realistic, high fidelity, $512\times512$ pixels, full, plain impression fingerprints. Our results suggest that the fingerprints generated by the CFG are unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality, while not revealing the underlying identities of the training data. We make the pre-trained CFG model and the synthetically generated dataset publicly available at https://github.com/keivanB/Clarkson_Finger_Gen

Related papers

• Scalable Fingerprinting of Large Language Models [46.26999419117367]
  We introduce a new method, dubbed Perinucleus sampling, to generate scalable, persistent, and harmless fingerprints. We demonstrate that this scheme can add 24,576 fingerprints to a Llama-3.1-8B model without degrading the model's utility.
  arXiv  Detail & Related papers  (2025-02-11T18:43:07Z)
• Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions [25.738682467090335]
  GenPrint is a framework to produce fingerprint images of various types while maintaining identity. GenPrint is not confined to replicating style characteristics from the training dataset alone. Results demonstrate the benefits of GenPrint in terms of identity preservation, explainable control, and universality of generated images.
  arXiv  Detail & Related papers  (2024-04-21T23:01:08Z)
• FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data [7.203557048672379]
  We present FPGAN-Control, an identity preserving image generation framework. We introduce a novel appearance loss that encourages disentanglement between the fingerprint's identity and appearance properties. We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity level, degree of appearance control, and low synthetic-to-real domain gap.
  arXiv  Detail & Related papers  (2023-10-29T14:30:01Z)
• RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images [45.73061833269094]
  We propose the first deep forgery detection approach for fingerprint images, which combines unique ridge features of fingerprint and generation artifacts of the GAN-generated images. Our proposed approach is effective and robust with low complexities.
  arXiv  Detail & Related papers  (2023-08-18T04:05:18Z)
• Comparative analysis of segmentation and generative models for fingerprint retrieval task [0.0]
  Fingerprints deteriorate in quality if the fingers are dirty, wet, injured or when sensors malfunction. This paper proposes a deep learning approach to address these issues using Generative (GAN) and models. In our research, the u-net model performed better than the GAN networks.
  arXiv  Detail & Related papers  (2022-09-13T17:21:14Z)
• Synthetic Latent Fingerprint Generator [47.87570819350573]
  Given a full fingerprint image (rolled or slap), we present CycleGAN models to generate multiple latent impressions of the same identity as the full print. Our models can control the degree of distortion, noise, blurriness and occlusion in the generated latent print images. Our approach for generating synthetic latent fingerprints can be used to improve the recognition performance of any latent matcher.
  arXiv  Detail & Related papers  (2022-08-29T18:02:02Z)
• Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection [106.5308793283895]
  fingerprint leakage from social media raises a strong desire for anonymizing shared images. To guard the fingerprint leakage, adversarial attack emerges as a solution by adding imperceptible perturbations on images. We propose FingerSafe, a hierarchical perceptual protective noise injection framework to address the mentioned problems.
  arXiv  Detail & Related papers  (2022-08-23T02:20:46Z)
• SpoofGAN: Synthetic Fingerprint Spoof Images [47.87570819350573]
  A major limitation to advances in fingerprint spoof detection is the lack of publicly available, large-scale fingerprint spoof datasets. This work aims to demonstrate the utility of synthetic (both live and spoof) fingerprints in supplying these algorithms with sufficient data.
  arXiv  Detail & Related papers  (2022-04-13T16:27:27Z)
• Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks [6.700873164609009]
  We propose a novel fingerprint synthesis and reconstruction framework based on the StyleGan2 architecture. We also derive a computational approach to modify the attributes of the generated fingerprint while preserving their identity. The proposed framework was experimentally shown to outperform contemporary state-of-the-art approaches for both fingerprint synthesis and reconstruction.
  arXiv  Detail & Related papers  (2022-01-17T00:18:00Z)
• PrintsGAN: Synthetic Fingerprint Generator [39.804969475699345]
  PrintsGAN is a synthetic fingerprint generator capable of generating unique fingerprints along with multiple impressions for a given fingerprint. We show the utility of the PrintsGAN generated by training a deep network to extract a fixed-length embedding from a fingerprint.
  arXiv  Detail & Related papers  (2022-01-10T22:25:10Z)
• Responsible Disclosure of Generative Models Using Scalable Fingerprinting [70.81987741132451]
  Deep generative models have achieved a qualitatively new level of performance. There are concerns on how this technology can be misused to spoof sensors, generate deep fakes, and enable misinformation at scale. Our work enables a responsible disclosure of such state-of-the-art generative models, that allows researchers and companies to fingerprint their models.
  arXiv  Detail & Related papers  (2020-12-16T03:51:54Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)

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.