55,042 research outputs found
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks
Recent work has proposed the concept of backdoor attacks on deep neural
networks (DNNs), where misbehaviors are hidden inside "normal" models, only to
be triggered by very specific inputs. In practice, however, these attacks are
difficult to perform and highly constrained by sharing of models through
transfer learning. Adversaries have a small window during which they must
compromise the student model before it is deployed. In this paper, we describe
a significantly more powerful variant of the backdoor attack, latent backdoors,
where hidden rules can be embedded in a single "Teacher" model, and
automatically inherited by all "Student" models through the transfer learning
process. We show that latent backdoors can be quite effective in a variety of
application contexts, and validate its practicality through real-world attacks
against traffic sign recognition, iris identification of lab volunteers, and
facial recognition of public figures (politicians). Finally, we evaluate 4
potential defenses, and find that only one is effective in disrupting latent
backdoors, but might incur a cost in classification accuracy as tradeoff
Traffic Signs Detection and Recognition System using Deep Learning
With the rapid development of technology, automobiles have become an
essential asset in our day-to-day lives. One of the more important researches
is Traffic Signs Recognition (TSR) systems. This paper describes an approach
for efficiently detecting and recognizing traffic signs in real-time, taking
into account the various weather, illumination and visibility challenges
through the means of transfer learning. We tackle the traffic sign detection
problem using the state-of-the-art of multi-object detection systems such as
Faster Recurrent Convolutional Neural Networks (F-RCNN) and Single Shot Multi-
Box Detector (SSD) combined with various feature extractors such as MobileNet
v1 and Inception v2, and also Tiny-YOLOv2. However, the focus of this paper is
going to be F-RCNN Inception v2 and Tiny YOLO v2 as they achieved the best
results. The aforementioned models were fine-tuned on the German Traffic Signs
Detection Benchmark (GTSDB) dataset. These models were tested on the host PC as
well as Raspberry Pi 3 Model B+ and the TASS PreScan simulation. We will
discuss the results of all the models in the conclusion section.Comment: 7 pages, 14 figures, 10 table
Prototypical Priors: From Improving Classification to Zero-Shot Learning
Recent works on zero-shot learning make use of side information such as
visual attributes or natural language semantics to define the relations between
output visual classes and then use these relationships to draw inference on new
unseen classes at test time. In a novel extension to this idea, we propose the
use of visual prototypical concepts as side information. For most real-world
visual object categories, it may be difficult to establish a unique prototype.
However, in cases such as traffic signs, brand logos, flags, and even natural
language characters, these prototypical templates are available and can be
leveraged for an improved recognition performance. The present work proposes a
way to incorporate this prototypical information in a deep learning framework.
Using prototypes as prior information, the deepnet pipeline learns the input
image projections into the prototypical embedding space subject to minimization
of the final classification loss. Based on our experiments with two different
datasets of traffic signs and brand logos, prototypical embeddings incorporated
in a conventional convolutional neural network improve the recognition
performance. Recognition accuracy on the Belga logo dataset is especially
noteworthy and establishes a new state-of-the-art. In zero-shot learning
scenarios, the same system can be directly deployed to draw inference on unseen
classes by simply adding the prototypical information for these new classes at
test time. Thus, unlike earlier approaches, testing on seen and unseen classes
is handled using the same pipeline, and the system can be tuned for a trade-off
of seen and unseen class performance as per task requirement. Comparison with
one of the latest works in the zero-shot learning domain yields top results on
the two datasets mentioned above.Comment: 12 Pages, 6 Figures, 2 Tables, in British Machine Vision Conference
(BMVC), 201
TWINs: Two Weighted Inconsistency-reduced Networks for Partial Domain Adaptation
The task of unsupervised domain adaptation is proposed to transfer the
knowledge of a label-rich domain (source domain) to a label-scarce domain
(target domain). Matching feature distributions between different domains is a
widely applied method for the aforementioned task. However, the method does not
perform well when classes in the two domains are not identical. Specifically,
when the classes of the target correspond to a subset of those of the source,
target samples can be incorrectly aligned with the classes that exist only in
the source. This problem setting is termed as partial domain adaptation (PDA).
In this study, we propose a novel method called Two Weighted
Inconsistency-reduced Networks (TWINs) for PDA. We utilize two classification
networks to estimate the ratio of the target samples in each class with which a
classification loss is weighted to adapt the classes present in the target
domain. Furthermore, to extract discriminative features for the target, we
propose to minimize the divergence between domains measured by the classifiers'
inconsistency on target samples. We empirically demonstrate that reducing the
inconsistency between two networks is effective for PDA and that our method
outperforms other existing methods with a large margin in several datasets
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
This work addresses the problem of semantic image segmentation of nighttime
scenes. Although considerable progress has been made in semantic image
segmentation, it is mainly related to daytime scenarios. This paper proposes a
novel method to progressive adapt the semantic models trained on daytime
scenes, along with large-scale annotations therein, to nighttime scenes via the
bridge of twilight time -- the time between dawn and sunrise, or between sunset
and dusk. The goal of the method is to alleviate the cost of human annotation
for nighttime images by transferring knowledge from standard daytime
conditions. In addition to the method, a new dataset of road scenes is
compiled; it consists of 35,000 images ranging from daytime to twilight time
and to nighttime. Also, a subset of the nighttime images are densely annotated
for method evaluation. Our experiments show that our method is effective for
model adaptation from daytime scenes to nighttime scenes, without using extra
human annotation.Comment: Accepted to International Conference on Intelligent Transportation
Systems (ITSC 2018
CapsNet comparative performance evaluation for image classification
Image classification has become one of the main tasks in the field of
computer vision technologies. In this context, a recent algorithm called
CapsNet that implements an approach based on activity vectors and dynamic
routing between capsules may overcome some of the limitations of the current
state of the art artificial neural networks (ANN) classifiers, such as
convolutional neural networks (CNN). In this paper, we evaluated the
performance of the CapsNet algorithm in comparison with three well-known
classifiers (Fisher-faces, LeNet, and ResNet). We tested the classification
accuracy on four datasets with a different number of instances and classes,
including images of faces, traffic signs, and everyday objects. The evaluation
results show that even for simple architectures, training the CapsNet algorithm
requires significant computational resources and its classification performance
falls below the average accuracy values of the other three classifiers.
However, we argue that CapsNet seems to be a promising new technique for image
classification, and further experiments using more robust computation resources
and re-fined CapsNet architectures may produce better outcomes
Over speed detection using Artificial Intelligence
Over speeding is one of the most common traffic violations. Around 41 million people are issued speeding tickets each year in USA i.e one every second. Existing approaches to detect over- speeding are not scalable and require manual efforts. In this project, by the use of computer vision and artificial intelligence, I have tried to detect over speeding and report the violation to the law enforcement officer. It was observed that when predictions are done using YoloV3, we get the best results
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images
Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance on a variety of computer vision tasks, particularly visual
classification problems, where new algorithms reported to achieve or even
surpass the human performance. In this paper, we examine whether CNNs are
capable of learning the semantics of training data. To this end, we evaluate
CNNs on negative images, since they share the same structure and semantics as
regular images and humans can classify them correctly. Our experimental results
indicate that when training on regular images and testing on negative images,
the model accuracy is significantly lower than when it is tested on regular
images. This leads us to the conjecture that current training methods do not
effectively train models to generalize the concepts. We then introduce the
notion of semantic adversarial examples - transformed inputs that semantically
represent the same objects, but the model does not classify them correctly -
and present negative images as one class of such inputs
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
In this work, we connect two distinct concepts for unsupervised domain
adaptation: feature distribution alignment between domains by utilizing the
task-specific decision boundary and the Wasserstein metric. Our proposed sliced
Wasserstein discrepancy (SWD) is designed to capture the natural notion of
dissimilarity between the outputs of task-specific classifiers. It provides a
geometrically meaningful guidance to detect target samples that are far from
the support of the source and enables efficient distribution alignment in an
end-to-end trainable fashion. In the experiments, we validate the effectiveness
and genericness of our method on digit and sign recognition, image
classification, semantic segmentation, and object detection.Comment: Accepted at CVPR 201
VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection
Although traffic sign detection has been studied for years and great progress
has been made with the rise of deep learning technique, there are still many
problems remaining to be addressed. For complicated real-world traffic scenes,
there are two main challenges. Firstly, traffic signs are usually small size
objects, which makes it more difficult to detect than large ones; Secondly, it
is hard to distinguish false targets which resemble real traffic signs in
complex street scenes without context information. To handle these problems, we
propose a novel end-to-end deep learning method for traffic sign detection in
complex environments. Our contributions are as follows: 1) We propose a
multi-resolution feature fusion network architecture which exploits densely
connected deconvolution layers with skip connections, and can learn more
effective features for the small size object; 2) We frame the traffic sign
detection as a spatial sequence classification and regression task, and propose
a vertical spatial sequence attention (VSSA) module to gain more context
information for better detection performance. To comprehensively evaluate the
proposed method, we do experiments on several traffic sign datasets as well as
the general object detection dataset and the results have shown the
effectiveness of our proposed method
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