8,442 research outputs found
A comprehensive study of the usability of multiple graphical passwords
Recognition-based graphical authentication systems (RBGSs) using
images as passwords have been proposed as one potential solution to the need
for more usable authentication. The rapid increase in the technologies requiring
user authentication has increased the number of passwords that users have to
remember. But nearly all prior work with RBGSs has studied the usability of a
single password. In this paper, we present the first published comparison of the
usability of multiple graphical passwords with four different image types:
Mikon, doodle, art and everyday objects (food, buildings, sports etc.). A longi-tudinal experiment was performed with 100 participants over a period of 8
weeks, to examine the usability performance of each of the image types. The re-sults of the study demonstrate that object images are most usable in the sense of
being more memorable and less time-consuming to employ, Mikon images are
close behind but doodle and art images are significantly inferior. The results of
our study complement cognitive literature on the picture superiority effect, vis-ual search process and nameability of visually complex images
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NAVI: Novel authentication with visual information
Text-based passwords, despite their well-known drawbacks, remain the dominant user authentication scheme implemented. Graphical password systems, based on visual information such as the recognition of photographs and / or pictures, have emerged as a promising alternative to the aggregate reliance on text passwords. Nevertheless, despite the advantages offered they have not been widely used in practice since many open issues need to be resolved. In this paper we propose a novel graphical password scheme, NAVI, where the credentials of the user are his username and a password formulated by drawing a route on a predefined map. We analyze the strength of the password generated by this scheme and present a prototype implementation in order to illustrate the feasibility of our proposal. Finally, we discuss NAVI’s security features and compare it with existing graphical password schemes as well as text-based passwords in terms of key security features, such aspassword keyspace, dictionary attacks and guessing attacks. The proposed scheme appears to have the same or better performance in the majority of the security features examined
Comparing the usability of doodle and Mikon images to be used as authenticators in graphical authentication systems
Recognition-based graphical authentication systems rely on the recognition of authenticator images by legitimate
users for authentication. This paper presents the results of a study that compared doodle images and Mikon images as
authenticators in recognition based graphical authentication systems taking various usability dimensions into account. The results of the usability evaluation, with 20 participants, demonstrated that users preferred Mikon to doodle images as authenticators in recognition based graphical authentication mechanisms. Furthermore, participants found it difficult to recognize doodle images during authentication as well as associate them with something meaningful. Our findings also show the need to consider the security offered by the images, especially their predictability
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Active authentication refers to the process in which users are unobtrusively
monitored and authenticated continuously throughout their interactions with
mobile devices. Generally, an active authentication problem is modelled as a
one class classification problem due to the unavailability of data from the
impostor users. Normally, the enrolled user is considered as the target class
(genuine) and the unauthorized users are considered as unknown classes
(impostor). We propose a convolutional neural network (CNN) based approach for
one class classification in which a zero centered Gaussian noise and an
autoencoder are used to model the pseudo-negative class and to regularize the
network to learn meaningful feature representations for one class data,
respectively. The overall network is trained using a combination of the
cross-entropy and the reconstruction error losses. A key feature of the
proposed approach is that any pre-trained CNN can be used as the base network
for one class classification. Effectiveness of the proposed framework is
demonstrated using three publically available face-based active authentication
datasets and it is shown that the proposed method achieves superior performance
compared to the traditional one class classification methods. The source code
is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201
Predictive models for multibiometric systems
Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations
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