7,811 research outputs found
Seamless and Secure VR: Adapting and Evaluating Established Authentication Systems for Virtual Reality
Virtual reality (VR) headsets are enabling a wide range of new
opportunities for the user. For example, in the near future users
may be able to visit virtual shopping malls and virtually join
international conferences. These and many other scenarios pose
new questions with regards to privacy and security, in particular
authentication of users within the virtual environment. As a first
step towards seamless VR authentication, this paper investigates
the direct transfer of well-established concepts (PIN, Android
unlock patterns) into VR. In a pilot study (N = 5) and a lab
study (N = 25), we adapted existing mechanisms and evaluated
their usability and security for VR. The results indicate that
both PINs and patterns are well suited for authentication in
VR. We found that the usability of both methods matched the
performance known from the physical world. In addition, the
private visual channel makes authentication harder to observe,
indicating that authentication in VR using traditional concepts
already achieves a good balance in the trade-off between usability
and security. The paper contributes to a better understanding of
authentication within VR environments, by providing the first
investigation of established authentication methods within VR,
and presents the base layer for the design of future authentication
schemes, which are used in VR environments only
Using Hover to Compromise the Confidentiality of User Input on Android
We show that the new hover (floating touch) technology, available in a number
of today's smartphone models, can be abused by any Android application running
with a common SYSTEM_ALERT_WINDOW permission to record all touchscreen input
into other applications. Leveraging this attack, a malicious application
running on the system is therefore able to profile user's behavior, capture
sensitive input such as passwords and PINs as well as record all user's social
interactions. To evaluate our attack we implemented Hoover, a proof-of-concept
malicious application that runs in the system background and records all input
to foreground applications. We evaluated Hoover with 40 users, across two
different Android devices and two input methods, stylus and finger. In the case
of touchscreen input by finger, Hoover estimated the positions of users' clicks
within an error of 100 pixels and keyboard input with an accuracy of 79%.
Hoover captured users' input by stylus even more accurately, estimating users'
clicks within 2 pixels and keyboard input with an accuracy of 98%. We discuss
ways of mitigating this attack and show that this cannot be done by simply
restricting access to permissions or imposing additional cognitive load on the
users since this would significantly constrain the intended use of the hover
technology.Comment: 11 page
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
Why Do People Adopt, or Reject, Smartphone Password Managers?
People use weak passwords for a variety of reasons, the most prescient of these being memory load and inconvenience. The motivation to choose weak passwords is even more compelling on Smartphones because entering complex passwords is particularly time consuming and arduous on small devices. Many of the memory- and inconvenience-related issues can be ameliorated by using a password manager app. Such an app can generate, remember and automatically supply passwords to websites and other apps on the phone. Given this potential, it is unfortunate that these applications have not enjoyed widespread adoption. We carried out a study to find out why this was so, to investigate factors that impeded or encouraged password manager adoption. We found that a number of factors mediated during all three phases of adoption: searching, deciding and trialling. The study’s findings will help us to market these tools more effectively in order to encourage future adoption of password managers
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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Do That, There: An Interaction Technique for Addressing In-Air Gesture Systems
When users want to interact with an in-air gesture system, they
must first address it. This involves finding where to gesture
so that their actions can be sensed, and how to direct their
input towards that system so that they do not also affect others
or cause unwanted effects. This is an important problem [6]
which lacks a practical solution. We present an interaction
technique which uses multimodal feedback to help users address
in-air gesture systems. The feedback tells them how
(“do that”) and where (“there”) to gesture, using light, audio
and tactile displays. By doing that there, users can direct their
input to the system they wish to interact with, in a place where
their gestures can be sensed. We discuss the design of our
technique and three experiments investigating its use, finding
that users can “do that” well (93.2%–99.9%) while accurately
(51mm–80mm) and quickly (3.7s) finding “there”
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