Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures

Abstract

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...

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

“…The touch sensor is also helpful in diagnosing Parkinson disease through touch accuracy analysis [ 123 , 124 ]. In addition, a digitizer for writing can be applied to biometric authentication through handwritten signature recognition [ 125 , 126 ].…”

“…Table 5 summarizes the sensing methods and targets using smartphones. CMOS + microphone Heart rate, SpO 2 b , blood pressure [112] CMOS + microphone + speaker Diet management [113] CMOS + strain gauge + display Blood pressure [110,114] CMOS + temperature sensor Temperature, heart rate [115] IMU c Sleep monitoring [116,117] IMU Gait analysis [118] Microphone Spirometry [111,119] Microphone Breathing sound analysis [120] Microphone Sleep monitoring [121] Ultrasonic sensor Biometric using fingerprint [122] Touch sensor Heart rate [123] Touch sensor Parkinson disease [124] Digitizer Biometrics using signature [125,126]…”