31,795 research outputs found
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
Improving random number generators by chaotic iterations. Application in data hiding
In this paper, a new pseudo-random number generator (PRNG) based on chaotic
iterations is proposed. This method also combines the digits of two XORshifts
PRNGs. The statistical properties of this new generator are improved: the
generated sequences can pass all the DieHARD statistical test suite. In
addition, this generator behaves chaotically, as defined by Devaney. This makes
our generator suitable for cryptographic applications. An illustration in the
field of data hiding is presented and the robustness of the obtained data
hiding algorithm against attacks is evaluated.Comment: 6 pages, 8 figures, In ICCASM 2010, Int. Conf. on Computer
Application and System Modeling, Taiyuan, China, pages ***--***, October 201
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
Grayscale Image Authentication using Neural Hashing
Many different approaches for neural network based hash functions have been
proposed. Statistical analysis must correlate security of them. This paper
proposes novel neural hashing approach for gray scale image authentication. The
suggested system is rapid, robust, useful and secure. Proposed hash function
generates hash values using neural network one-way property and non-linear
techniques. As a result security and performance analysis are performed and
satisfying results are achieved. These features are dominant reasons for
preferring against traditional ones.Comment: international journal of Natural and Engineering Sciences
(NESciences.com) : Image Authentication, Cryptology, Hash Function,
Statistical and Security Analysi
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