108,492 research outputs found
From Codes to Patterns: Designing Interactive Decoration for Tableware
ABSTRACT
We explore the idea of making aesthetic decorative patterns that contain multiple visual codes. We chart an iterative collaboration with ceramic designers and a restaurant to refine a recognition technology to work reliably on ceramics, produce a pattern book of designs, and prototype sets of tableware and a mobile app to enhance a dining experience. We document how the designers learned to work with and creatively exploit the technology, enriching their patterns with embellishments and backgrounds and developing strategies for embedding codes into complex designs. We discuss the potential and challenges of interacting with such patterns. We argue for a transition from designing âcodes to patternsâ that reflects the skills of designers alongside the development of new technologies
Spatial Interference Detection for Mobile Visible Light Communication
Taking advantage of the rolling shutter effect of CMOS cameras in smartphones
is a common practice to increase the transfered data rate with visible light
communication (VLC) without employing external equipment such as photodiodes.
VLC can then be used as replacement of other marker based techniques for object
identification for Augmented Reality and Ubiquitous computing applications.
However, the rolling shutter effect only allows to transmit data over a single
dimension, which considerably limits the available bandwidth. In this article
we propose a new method exploiting spacial interference detection to enable
parallel transmission and design a protocol that enables easy identification of
interferences between two signals. By introducing a second dimension, we are
not only able to significantly increase the available bandwidth, but also
identify and isolate light sources in close proximity
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks
Malware still constitutes a major threat in the cybersecurity landscape, also
due to the widespread use of infection vectors such as documents. These
infection vectors hide embedded malicious code to the victim users,
facilitating the use of social engineering techniques to infect their machines.
Research showed that machine-learning algorithms provide effective detection
mechanisms against such threats, but the existence of an arms race in
adversarial settings has recently challenged such systems. In this work, we
focus on malware embedded in PDF files as a representative case of such an arms
race. We start by providing a comprehensive taxonomy of the different
approaches used to generate PDF malware, and of the corresponding
learning-based detection systems. We then categorize threats specifically
targeted against learning-based PDF malware detectors, using a well-established
framework in the field of adversarial machine learning. This framework allows
us to categorize known vulnerabilities of learning-based PDF malware detectors
and to identify novel attacks that may threaten such systems, along with the
potential defense mechanisms that can mitigate the impact of such threats. We
conclude the paper by discussing how such findings highlight promising research
directions towards tackling the more general challenge of designing robust
malware detectors in adversarial settings
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