Similarity Analysis of Actual Fake Fingerprints and Generated Fake Fingerprints by DCGAN

Choi, Seoung-Ho; Jung, Sung Hoon

doi:10.5391/ijfis.2019.19.1.40

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…In the first category of the literature survey, we focused on the reference papers that concentrated on generated quality biometrics. Choi et al [14] generated quality synthetic fingerprint samples. Using numerous evaluating metrics such as Hamming distance, Pearson correlation of histograms, and Intersection of Union, experiments found the generated fingerprints to be of quality to that of actual fake fingerprint.…”

Section: Discussionmentioning

confidence: 99%

“…In Choi et al [14], a verification method is proposed to evaluate the quality of fake fingerprints generated by DCGAN. The synthetic generated fingerprints are compared to non-generated fake fingerprints through four similarity measures.…”

Section: Generating Quality Biometricsmentioning

confidence: 99%

“…The fourth metric, intersection of union (IOU), is used to assess the shape similarity of the generated synthetic fingerprints and non-generated fake fingerprints. From the immense experiments, in terms of the four similarity measures, it was concluded that the DCGAN generated synthetic fingerprints could be used to enhance the fake fingerprint data [14].…”

Section: Generating Quality Biometricsmentioning

confidence: 99%

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Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study

Jenkins,

Roy

2024

Discov Artif Intell

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Over the past few years, there has been a proliferation of research in the area of generative adversarial networks (GANs). GANs present a novel approach to producing synthetic data in varying fields including medicine, traffic control, text transferring, image generation, and cybersecurity. To improve the quality of synthetic generation, specifically for images, the GAN technique was paired with convolutional neural networks (CNNs) to build deep convolutional generative adversarial networks (DCGAN). The DCGAN framework is a simple yet stable framework shown to generate quality photorealistic images. There are a number of studies reviewing GANs, providing a comparative analysis of performance, stabilization, and training methods. With respects to the DCGAN architecture, there are literature reviews reporting its usage in forensic sketch to face transformation and fuzzy face recognition. Here, we provide a review detailing the use of the DCGAN framework with biometrics samples for advancements in biometric authentication systems and cybersecurity. As GANs have shown to be a primary tool in generating deepfakes, we explore the use of DCGANs to generating synthetic biometrics that can deceive security systems and serve as quality training data for other machine learning models. The goal of this review is to contribute a concise consolidated review of techniques involving the DCGAN framework and biometric samples for the improvement of biometric recognition systems and to be used as a reference point for future work in cybersecurity.

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Section: Discussionmentioning

confidence: 99%

Section: Generating Quality Biometricsmentioning

confidence: 99%

Section: Generating Quality Biometricsmentioning

confidence: 99%

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Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study

Jenkins,

Roy

2024

Discov Artif Intell

View full text Add to dashboard Cite

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“…This paper employs the Hamming distance of information theory, which is a well-known measure designed to provide insights into the similarity of information [33,34] and has been widely employed in distance measures [26,35], to measure the determinacy degree and conflict degree. Before this, we present the concept of a minimum conflict neutrosophic number and maximum conflict neutrosophic number.…”

Section: The Measures Of Determinacy Degree and Conflict Degreementioning

confidence: 99%

Algorithm for Neutrosophic Soft Sets in Stochastic Multi-Criteria Group Decision Making Based on Prospect Theory

2019

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To address issues involving inconsistencies, this paper proposes a stochastic multi-criteria group decision making algorithm based on neutrosophic soft sets, which includes a pair of asymmetric functions: Truth-membership and false-membership, and an indeterminacy-membership function. For integrating an inherent stochastic, the algorithm expresses the weights of decision makers and parameter subjective weights by neutrosophic numbers instead of determinate values. Additionally, the algorithm is guided by the prospect theory, which incorporates psychological expectations of decision makers into decision making. To construct the prospect decision matrix, this research establishes a conflict degree measure of neutrosophic numbers and improves it to accommodate the stochastic multi-criteria group decision making. Moreover, we introduce the weighted average aggregation rule and weighted geometric aggregation rule of neutrosophic soft sets. Later, this study presents an algorithm for neutrosophic soft sets in the stochastic multi-criteria group decision making based on the prospect theory. Finally, we perform an illustrative example and a comparative analysis to prove the effectiveness and feasibility of the proposed algorithm.

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“…An imbalanced fault diagnosis method based on the generative model of DCGAN was proposed by Luo et al [23] to solve the problems of limited datasets. In order to expand fake fingerprint data, Choi et al [24] proposed a method to investigate whether a fake fingerprint generated by DCGAN was similar to a fake fingerprint from the dataset. At present, image generation technology is still relatively rare in the field of roads, and the intelligent detection of road cracks is also in its exploratory stages.…”

Section: Introductionmentioning

confidence: 99%

A Rapid Bridge Crack Detection Method Based on Deep Learning

Liu,

Gao,

Zhao

et al. 2023

Applied Sciences

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The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concrete crack images were generated by a deep convolutional generative adversarial network (DCGAN), and the artificial samples were balanced and feature-rich. Then, the dataset was established by mixing the artificial samples with the original samples. You Only Look Once v5 (YOLOv5) was trained on this dataset to implement rapid detection of concrete bridge cracks, and the detection accuracy was compared with the results using only the original samples. The experiments show that DCGAN can mine the potential distribution of image data and extract crack features through the deep transposed convolution layer and down sampling operation. Moreover, the light-weight YOLOv5 increases channel capacity and reduces the dimensions of the input image without losing pixel information. This method maintains the generalization performance of the neural network and provides an alternative solution with a low cost of data acquisition while accomplishing the rapid detection of bridge cracks with high precision.

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Similarity Analysis of Actual Fake Fingerprints and Generated Fake Fingerprints by DCGAN

Cited by 7 publications

References 9 publications

Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study

Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study

Algorithm for Neutrosophic Soft Sets in Stochastic Multi-Criteria Group Decision Making Based on Prospect Theory

A Rapid Bridge Crack Detection Method Based on Deep Learning

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