33,286 research outputs found
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
The World Health Organization (WHO) reported 1.25 million deaths yearly due
to road traffic accidents worldwide and the number has been continuously
increasing over the last few years. Nearly fifth of these accidents are caused
by distracted drivers. Existing work of distracted driver detection is
concerned with a small set of distractions (mostly, cell phone usage).
Unreliable ad-hoc methods are often used.In this paper, we present the first
publicly available dataset for driver distraction identification with more
distraction postures than existing alternatives. In addition, we propose a
reliable deep learning-based solution that achieves a 90% accuracy. The system
consists of a genetically-weighted ensemble of convolutional neural networks,
we show that a weighted ensemble of classifiers using a genetic algorithm
yields in a better classification confidence. We also study the effect of
different visual elements in distraction detection by means of face and hand
localizations, and skin segmentation. Finally, we present a thinned version of
our ensemble that could achieve 84.64% classification accuracy and operate in a
real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949
Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"
Large-scale variations still pose a challenge in unconstrained face
detection. To the best of our knowledge, no current face detection algorithm
can detect a face as large as 800 x 800 pixels while simultaneously detecting
another one as small as 8 x 8 pixels within a single image with equally high
accuracy. We propose a two-stage cascaded face detection framework, Multi-Path
Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a
deep neural network with a classic learning strategy, to tackle this challenge.
The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes
faces at three different scales. It simultaneously utilizes three parallel
outputs of the convolutional feature maps to predict multi-scale candidate face
regions. The "atrous" convolution trick (convolution with up-sampled filters)
and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN
to further boost its performance. The second stage is a Boosted Forests
classifier, which utilizes deep facial features pooled from inside the
candidate face regions as well as deep contextual features pooled from a larger
region surrounding the candidate face regions. This step is included to further
remove hard negative samples. Experiments show that this approach achieves
state-of-the-art face detection performance on the WIDER FACE dataset "hard"
partition, outperforming the former best result by 9.6% for the Average
Precision.Comment: 11 pages, 7 figures, to be presented at CRV 201
Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating
In this work, we investigate the concept of biometric backdoors: a template
poisoning attack on biometric systems that allows adversaries to stealthily and
effortlessly impersonate users in the long-term by exploiting the template
update procedure. We show that such attacks can be carried out even by
attackers with physical limitations (no digital access to the sensor) and zero
knowledge of training data (they know neither decision boundaries nor user
template). Based on the adversaries' own templates, they craft several
intermediate samples that incrementally bridge the distance between their own
template and the legitimate user's. As these adversarial samples are added to
the template, the attacker is eventually accepted alongside the legitimate
user. To avoid detection, we design the attack to minimize the number of
rejected samples.
We design our method to cope with the weak assumptions for the attacker and
we evaluate the effectiveness of this approach on state-of-the-art face
recognition pipelines based on deep neural networks. We find that in scenarios
where the deep network is known, adversaries can successfully carry out the
attack over 70% of cases with less than ten injection attempts. Even in
black-box scenarios, we find that exploiting the transferability of adversarial
samples from surrogate models can lead to successful attacks in around 15% of
cases. Finally, we design a poisoning detection technique that leverages the
consistent directionality of template updates in feature space to discriminate
between legitimate and malicious updates. We evaluate such a countermeasure
with a set of intra-user variability factors which may present the same
directionality characteristics, obtaining equal error rates for the detection
between 7-14% and leading to over 99% of attacks being detected after only two
sample injections.Comment: 12 page
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
We present an integrated framework for using Convolutional Networks for
classification, localization and detection. We show how a multiscale and
sliding window approach can be efficiently implemented within a ConvNet. We
also introduce a novel deep learning approach to localization by learning to
predict object boundaries. Bounding boxes are then accumulated rather than
suppressed in order to increase detection confidence. We show that different
tasks can be learned simultaneously using a single shared network. This
integrated framework is the winner of the localization task of the ImageNet
Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very
competitive results for the detection and classifications tasks. In
post-competition work, we establish a new state of the art for the detection
task. Finally, we release a feature extractor from our best model called
OverFeat
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