Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection

Souza, Gustavo Botelho de; Santos, D. R. dos; Pires, Rafael G.; Marana, Aparecido Nilceu; Papa, João Paulo

doi:10.2478/jaiscr-2018-0023

Cited by 34 publications

(6 citation statements)

References 14 publications

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“…In the proposed method, the running time for 18,000 features is increased to 45 s which is slightly high which is still the future direction to reduce the time computation. While, the running time of UniNap1 [31], ATVS [32], Dermalog [31], CAoS [32] time of the proposed method is faster than other methods.…”

Section: Resultsmentioning

confidence: 89%

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A New Method to Enhance Fingerprint Image Reconstruction Using Deep Boltzmann Machine

Alotaibi¹

2020

IJIES

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A new algorithm for increasing fingerprint with large noise is enhanced in this paper. The fingerprint image is first pre-improved depending on Gabor filters, and local adaptive thresholds are used to achieve a binary fingerprint. In order to reconstruct these regions, which have incorrectly improved in the first stage, the classification deep Boltzmann machines (DBMs) with a range pattern before are used. The proposed technique completely enhances each other by a traditional technique of improvement relying upon Gabor filtering and deep learning. The FVC 2004, Biometrika, Italdata, Crossmatch, and Swipe databases are conducted by different techniques. Experiments indicate that in contrast with other techniques, the suggested technique achieves stronger outcomes and enhances fingerprint performance. Experimental findings indicate that the use of the proposed technique allows the extremely accurate fingerprint image to be reconstructed. The average performance of the proposed method is 91.31, which is better than the average performance of another method. When there exist 18,000 features, the running time of UniNap1, ATVS, Dermalog, and CAoS are 68, 74, 59, 87 s respectively, while the running time of the proposed method is 45s.The proposed technique is superior for removing noise than other techniques, in particular for improving fingerprints of poor performance.

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Section: Resultsmentioning

confidence: 89%

“…Table 3 demonstrates the detection accuracy evaluated with Biometrika, Italdata, Crossmatch, and Swipe datasets. The average classification error (ACE) is used to evaluate the performance of the UniNap1 [31], ATVS [32], Dermalog [31], CAoS [32], and proposed methods. From Table 3, we note that the proposed method is better than other methods.…”

Section: Resultsmentioning

confidence: 99%

A New Method to Enhance Fingerprint Image Reconstruction Using Deep Boltzmann Machine

Alotaibi¹

2020

IJIES

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“…Ehret et al in [19] introduced a technique that relies on SIFT, which provides sparse keypoints with scale, rotation, and illumination invariant descriptors for forgery detection. A method for fingerprint faking detection utilizing deep Boltzmann machines (DBM) for image analysis of high-level characteristics is proposed in [20]. Balsa et al in [21] compared the DCT, Walsh-Hadamard transform (WHT), Haar wavelet transform (DWT), and discrete Fourier transform (DFT) for analog image transmission, changing compression and comparing quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Forgery Detection Using Deep Learning by Recompressing Images

Ali

Ganapathi

et al. 2022

Electronics

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Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning-based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%.

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“…The system's task is to monitor the activity within a network of connected computers so as to analyze the activity of intrusive patterns. In [23], an attempt was made to detect fraud in biometric systems. To detect this type of fraud, the authors propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for the extraction of high-level features from images.…”

Section: Related Workmentioning

confidence: 99%

Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic

Gabryel

Grzanek

Hayashi

2020

Journal of Artificial Intelligence and Soft Computing Research

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Web-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extracting multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash function. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, making it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device parameters. The paper presents methods that significantly limit this type of activity. New algorithms for coding and comparing fingerprints are presented, in which the values of parameters with low stability and low entropy are especially taken into account. The fingerprint generation methods are based on popular Minhash, the LSH, and autoencoder methods. The effectiveness of coding and comparing each of the presented methods was also examined in comparison with the currently used hash generation method. Authentic data of the devices and browsers of users visiting 186 different websites were collected for the research.

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Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection

Cited by 34 publications

References 14 publications

A New Method to Enhance Fingerprint Image Reconstruction Using Deep Boltzmann Machine

A New Method to Enhance Fingerprint Image Reconstruction Using Deep Boltzmann Machine

Image Forgery Detection Using Deep Learning by Recompressing Images

Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic

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