9,325 research outputs found
Developing and Testing a Visual Hash Scheme
Users find comparing long meaningless strings of alphanumeric characters difficult, yet they have to carry out this task when comparing cryptographic hash values for https certificates and PGP keys, or in the context of electronic voting. Visual hashes - where users compare images rather than strings - have been proposed as an alternative. With the visual hashes available in literature, however, people are unable to sufficiently distinguish more than 30 bits. Obviously, this does not provide adequate security against collision attacks. Our goal is to improve the situation: a visual hash scheme was developed, evaluated through pilot user studies and improved iteratively, leading to CLPS, which encodes 60 distinguishable bits using Colours, Patterns and Shapes. In the final user study, participants attained an average accuracy rate of 97% when comparing two visual hash images, with one placed above the other. CLPS was further tested in two follow-up studies, simulating https certificate validation and verifying in remote electronic voting. The results of this work and their implications for practical applications of visual hash schemes are discussed
Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop
Existing fine-grained visual categorization methods often suffer from three
challenges: lack of training data, large number of fine-grained categories, and
high intraclass vs. low inter-class variance. In this work we propose a generic
iterative framework for fine-grained categorization and dataset bootstrapping
that handles these three challenges. Using deep metric learning with humans in
the loop, we learn a low dimensional feature embedding with anchor points on
manifolds for each category. These anchor points capture intra-class variances
and remain discriminative between classes. In each round, images with high
confidence scores from our model are sent to humans for labeling. By comparing
with exemplar images, labelers mark each candidate image as either a "true
positive" or a "false positive". True positives are added into our current
dataset and false positives are regarded as "hard negatives" for our metric
learning model. Then the model is retrained with an expanded dataset and hard
negatives for the next round. To demonstrate the effectiveness of the proposed
framework, we bootstrap a fine-grained flower dataset with 620 categories from
Instagram images. The proposed deep metric learning scheme is evaluated on both
our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show
significant performance gain using dataset bootstrapping and demonstrate
state-of-the-art results achieved by the proposed deep metric learning methods.Comment: 10 pages, 9 figures, CVPR 201
Opaque Service Virtualisation: A Practical Tool for Emulating Endpoint Systems
Large enterprise software systems make many complex interactions with other
services in their environment. Developing and testing for production-like
conditions is therefore a very challenging task. Current approaches include
emulation of dependent services using either explicit modelling or
record-and-replay approaches. Models require deep knowledge of the target
services while record-and-replay is limited in accuracy. Both face
developmental and scaling issues. We present a new technique that improves the
accuracy of record-and-replay approaches, without requiring prior knowledge of
the service protocols. The approach uses Multiple Sequence Alignment to derive
message prototypes from recorded system interactions and a scheme to match
incoming request messages against prototypes to generate response messages. We
use a modified Needleman-Wunsch algorithm for distance calculation during
message matching. Our approach has shown greater than 99% accuracy for four
evaluated enterprise system messaging protocols. The approach has been
successfully integrated into the CA Service Virtualization commercial product
to complement its existing techniques.Comment: In Proceedings of the 38th International Conference on Software
Engineering Companion (pp. 202-211). arXiv admin note: text overlap with
arXiv:1510.0142
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