On Hunting Animals of the Biometric Menagerie for Online Signature

Houmani, Nesma; Garcia-Salicetti, Sonia

doi:10.1371/journal.pone.0151691

Cited by 19 publications

(31 citation statements)

References 36 publications

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“…In order to assess signature vulnerability to attacks, we acquired 10 skilled forgeries per signature type after displaying on the screen the shape and kinematics of the target signature. This type of forgery is considered in the literature as being the best attacks [3,43,44]. We thus obtain 3700 skilled forgeries (74 × 10 × 5) done by different forgers.…”

Section: Signature Data Acquisitionmentioning

confidence: 99%

“…These signatures are called "Reference signatures". In our previous works on signature quality assessment, we have shown that a signature's resistance to attacks depends on its information content, quantified by an entropy-based measure, called personal entropy [28,[40][41][42][43]. We identified automatically different risk levels in signatures related to three user categories, and in particular a "problematic" population, characterized by simple and highly variable signatures, very vulnerable to attacks.Based on these findings, we propose in this paper, since the enrollment phase on a touch screen sensor, a novel strategy that turns any signature with a "high risk" into a "low risk" one.…”

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confidence: 99%

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Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures

Abazid

Houmani

Garcia-Salicetti

2020

Sensors

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We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a novel and original strategy to reinforce identity security by enhancing signature resistance to attacks, assessed per signature category, both in terms of data quality and verification performance. This strategy operates upstream from the verification system, at the sensor level, by enriching the information content of signatures with personal handwritten inputs of different types. We study this strategy on different signature types of 74 users, acquired in uncontrolled mobile conditions on a largely deployed mobile touch-screen sensor. Our analysis per writer category revealed that adding alphanumeric (date) and handwriting (place) information to the usual signature is the most powerful augmented signature type in terms of verification performance. The relative improvement for all user categories is of at least 93% compared to the usual signature.Sensors 2020, 20, 933 2 of 21 sensor technology, interoperability, setting several new challenging issues that impact verification performance [2,17].Usually, for improving verification performance, different strategies were exploited in the literature: (i) acquiring signatures in controlled conditions [1-16]; (ii) using a high quality sensor (such as a Wacom tablet) with high temporal and spatial resolution, and able to capture other time functions than pen coordinates, as pen pressure and pen inclination angles [18]; (iii) selecting reference signatures in order to control intra-personal variability [19][20][21]; (iv) extracting several features for signature description (as pressure, speed, and acceleration, etc.) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] or by means of a deep neural network [2,[22][23][24][25].However, some of these strategies are no longer possible in the mobile scenario: as pointed out by [17], the sensors are not of the same quality, in terms of temporal resolution in particular, acquisition conditions are highly variable, and some sensors are limited to the capture of only pen coordinates. In the so-called "cloud scenario" [17], users acquire their signatures as they want, standing, sitting or moving, handling the device on the hand at different angles or orientations, or placing it on any support. A smartphone is usually handheld, while a tablet may be placed on the desktop or sustained by the left arm if the writer is right-handed. The consequence is that verification performance is strongly degraded in mobile conditions [2,15,16,[26][27][2...

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Section: Signature Data Acquisitionmentioning

confidence: 99%

mentioning

confidence: 99%

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures

Abazid

Houmani

Garcia-Salicetti

2020

Sensors

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“…The phenomenon of a "biometric menagerie", or "biometric zoo", has been observed in many biometric modalities and across several datasets [40,11]. These terms refer to the observation that the contribution of each user towards the overall FAR and FRR of a biometric system can vary significantly.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, it is difficult to refine selection strategies to deal with problematic templates [25] thereby limiting the value of the quality metric. In addition, the concept of a user's personal entropy and relative entropy to infer a signature template's FRR and FAR against skilled forgery has recently been introduced by Houmani et al, in 2016 [11]. In their work, the proposed user's relative entropy for each signature template is derived from Kullback-Leibler distance between the local probability density functions of HMM model of genuine samples and that of skilled forgeries.…”

Section: Related Workmentioning

confidence: 99%

Distinctiveness, complexity, and repeatability of online signature templates

Sae-Bae¹,

Memon²,

Sooraksa³

2018

Pattern Recognition

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This paper proposes three measures to quantify the characteristics of online signature templates in terms of distinctiveness, complexity and repeatability. A distinctiveness measure of a signature template is computed from a set of enrolled signature samples and a statistical assumption about random signatures. Secondly, a complexity measure of the template is derived from a set of enrolled signature samples. Finally, given a signature template, a measure to quantify the repeatability of the online signature is derived from a validation set of samples. These three measures can then be used as an indicator for the performance of the system in rejecting random forgery samples and skilled forgery samples and the performance of users in providing accepted genuine samples, respectively. The effectiveness of these three measures and their applications are demonstrated through experiments performed on three online signature datasets and one keystroke dynamics dataset using different verification algorithms.

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“…Houmani and Garcia-Salicetti [18] extended the Biometric Menagerie to online signatures and categorized the users of MCYT database using the Personal Entropy quality measure.…”

Section: Signaturementioning

confidence: 99%

Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus

Antal

Szabó

Tordai

2018

Mobile Information Systems

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We present MOBISIG, a pseudosignature dataset containing finger-drawn signatures from 83 users captured with a capacitive touchscreen-based mobile device. e database was captured in three sessions resulting in 45 genuine signatures and 20 skilled forgeries for each user. e database was evaluated by two state-of-the-art methods: a function-based system using local features and a feature-based system using global features. Two types of equal error rate computations are performed: one using a global threshold and the other using user-specific thresholds. e lowest equal error rate was 0.01% against random forgeries and 5.81% against skilled forgeries using user-specific thresholds that were computed a posteriori. However, these equal error rates were significantly raised to 1.68% (random forgeries case) and 14.31% (skilled forgeries case) using global thresholds. e same evaluation protocol was performed on the DooDB publicly available dataset. Besides verification performance evaluations conducted on the two finger-drawn datasets, we evaluated the quality of the samples and the users of the two datasets using basic quality measures. e results show that finger-drawn signatures can be used by biometric systems with reasonable accuracy.

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On Hunting Animals of the Biometric Menagerie for Online Signature

Cited by 19 publications

References 36 publications

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures

Distinctiveness, complexity, and repeatability of online signature templates

Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus

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